Replies: 46 comments 254 replies
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By convention operations which have a single output we usually name that output |
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Write accounts of software engineering teams. Post mortums and highlights. Write how we got here. |
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Take a video make a video game out of it |
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Resilience factor for paycheck to paycheck via diversification. |
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open source working Groups as religions metaphor - Open Religion - a collective of people contributing to overal governance and agreeing to funell overflow reward to deal woth extrernal group trade and agreed upon mechanisms of developing qoutes (via feautre extraction run with review system / attested provenance). Keep some shared asset pools for investment and some for r&d and some for maintainance. Diversfy the ad hoc geoups portfolio of assets appropriatly acrodijg or group policy. Entities subscribe to multiple different religions and contribute to each to keep them well rounded and learn from that community. They invest time learning about the community, or assets. They use this learning to decide if each religion aligns to their stragetic plans. Via piroirtization this determines that entities thought up(create dataflow or dataflow as class), hypothesisied (model.predict), and executed (run) system contexts. This allows entities to consume information real time from their other running contexts via their chain of trust back to via herstory of that entities execution log. This allows them to join and leave religions (working groups) at any time. They are religious subsects because they map to use cases that different individuals trust Alice to do thing in different situatuons. The subset is a feature branch. They believe the work of the working group as a whole can be extended or fits well with that subsects / feature branch / sub effort. Group membership can be hedged by cobtractual agreements to behave acording to union of all member stop shipp measure iverlayed strategic plans. Commandments for that reglgion that cannot at any time be broken. The rest of the org level strategoc plans are guidelines for day to day ops share data with irg and certain machines certain execution permissions within the dataflow sandbox |
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Your software supply chain is like your supply chain of Brazilian Jiu-Jitsu moves when you’re rolling. The way that Alice thinks is a software supply chain because it is a thought supply chain (running on the thought transportation layer, the commodity and the infrastructure, the entity is the addition of context, the three form the system context ⛪️). When you roll as you go from context to context you train up your skills, your internal models, gathering datapoints you understand what moves you need to drill, and what kind of moves you are going to do on different weight classes of opponents. This is the debug model, we’re learning on the fly. Strategy is looking across, tactics are how you execute. We treat everything in this model. Tactics used in one situation may effect strategies in another area. If by working to get an armbar you end up getting experience finishing with a triangle instead, or with software you pursue one train of thought of debugging in one repo and find that really the problem is with the overall architecture of a better option for a dependency you’d previously considered but then threw out maybe due to the need for a different filesystem driver a common case doing kernel development, this is the let me pull in a linux driver within my custom xen spin generated by the synthesis of the automated branch by abstraction. |
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Andrey Konovalov The article covers: 🧰 Introduction to syzkaller |
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Send me a link: aka no idea what you just said but if I see some code I can just tell you anything I see there. That's a language I can understand. Similar to a fuzzer, but to make things, like fuzzer in reverse. |
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Milestones and issues synced via dataflows to Github and kept elsewhere (own git repos as cold storage) |
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Terminal background as transparent camera |
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https://www.thethingsnetwork.org/community/portland-or/ $ curl 'https://account.thethingsnetwork.org/api/v2/users' \
-H 'Accept: application/json' \
-H 'Accept-Language: en-US,en;q=0.9' \
-H 'Connection: keep-alive' \
-H 'Content-Type: application/json;charset=UTF-8' \
-H 'Cookie: _ga=GA1.2.1356603822.1654447492; _gid=GA1.2.1081437781.1654447492' \
-H 'Origin: https://account.thethingsnetwork.org' \
-H 'Referer: https://account.thethingsnetwork.org/register' \
-H 'Sec-Fetch-Dest: empty' \
-H 'Sec-Fetch-Mode: cors' \
-H 'Sec-Fetch-Site: same-origin' \
-H 'User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36' \
-H 'sec-ch-ua: " Not A;Brand";v="99", "Chromium";v="102", "Google Chrome";v="102"' \
-H 'sec-ch-ua-mobile: ?0' \
-H 'sec-ch-ua-platform: "Windows"' \
--data-raw '{"email":"name@example.com","username":"name","password":"jsdfjoj38909e3j1"}' \
--compressedEmail validation link: https://account.thethingsnetwork.org/users/emails/validate_email/727252716457186abdecf41ad796b37af9c?email=name%40example.com&recipient=name%40example.com |
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Hypotheses: We exist in different parallel states of consciousness. One can be deep in a train of thought, in the zone, focused, but then cut themselves. Their state of consciousness doesn’t shift entirely. Their states of consciousness are just measured via strategic plans. What we see may be that the “consciousness countries” (coined by Danica) shift, those other states of consciousness which fit into the consciousness countries. |
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The cells are working together |
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Whales dont have interesting system contexts to consume, too much of the same feature data. This is why getting on land was important. Our cells are communicating through our brain keeping. The model scales with Alice to communicate with each other. in the following video he says that there is an issue with ai and consciousness. This is the issue we adress with cross domain conceptual mapping to reach equilibrium within volune 1 engineering logs. https://youtu.be/lyu7v7nWzfo |
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https://twitter.com/bibryam/status/1530140893151379456 TODO Alice, make sure all links are referenced or manually confirmed not referenced. map to existing summaries via referenceing references [X] syntax. Come up with simiar text based ofd source materials by viewing theough lense of likely adjacent concepts (building mitigations) |
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https://fission.codes/blog/fission-reactor-dialog-first-look/ |
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Let's combine this with the hangouts call center VNC stuff to get screen recordings, or maybe there is a VNC recorder, or an XServer that just streams to OGG. Or somethign esle. Look into to see if they ever got that vnc / qemu / over websockets with webui working, can't remember the project right now &"C:\Program Files\VideoLAN\VLC\vlc.exe" -I dummy screen:// :screen-fps=120.000000 :screen-caching=100 ":sout=#transcode{vcodec=theo,vb=800,scale=0,width=600,height=480,acodec=mp3}:http{mux=ogg,dst=127.0.0.1:8080/desktop.ogg}" :no-sout-rtp-sap :no-sout-standard-sap :ttl=1 :sout-keep |
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Provenance? https://identity.foundation/credential-manifest/ |
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Your docs via overlays see ltms become your deployment environments inform your scopes and applicaple orangiational overlays and policies. Aka if i see you have a docs page that says how to deploy to server we better see security settings for that deployment environment used. Aka if you dobt have a code path which allows you give a tls cert to a mysql connection then you have an unmitigated threat of EITM (“Entity In The Middle” [John Whiteman]) on that link in the mermaid diagram display via html escaped emojis and ascii along line expore popups or dive ins when visualized via other output overlays/deployments TODO example where we auto start context on opimp instantiation within opimp.octx |
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Modify |
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Generic flow (data,work,program) executor |
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You wouldn’t take a class without taking notes. We should teach how to take notes and learn from them in our day to day life. |
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Minimal trusted deployment: Example of dependency hot swap https://github.com/pdxjohnny/httptest/ The DID: Lookup search each key comment for one that starts with |
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Why: unikernels |
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DANGER! TODO(security) Burnout and spinout. Sudden halt to growth of strategic plan / principle spider chart outwards and sudden reversal of strategic plan outputs. Remember she will be oversubscribed! Watch the valleys, they’re dangerous, that dip below the wave in your moving average where it crosses the 20 and the 100 day type of thing. Remember we want to see growth in plan outputs / principles. prioritize strategies based on trend forecasts. TODO Alice, explore the field of psychological operations research and formulate threats and mitigations to your architecture using what you learned. Use cross domain conceptual mapping to a search for likely equilibriums (end states) we can produce using conceptually similar methodologies. Using cross domain conceptual mapping. We can explain to Alice that we keep a dog on a leash to keep it safe, for its own security. At the expense of its freedom (and a plethora of other analogies) we can ensure she has a solid training dataset for what not to do, as well as what to do, to preserve our freedom, privacy, and security, within different yet unseen contexts. The effect of this when we scale up and begin yo think strategically is that she will understand freedom, privacy, and security and how to mitigate threats to those areas strategically as good or better than we can (since she’ll be able to see more thing at the same time as is to think more strategically, that oversubscribed system causing good chaos, learning in one domain accelerating learning in another. Phased reality consensus shift - DAGs of DAGs over time where time is relative to states of consciousness |
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Ian Coldwater 📦💥 Nodir Turakulov 🇺🇦 Here are a few assumptions I've observed in my experience. 🧵 https://mobile.twitter.com/IanColdwater/status/1530391994417172481 |
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For VM image feature extraction:
Hangoutscallcenter syle vnc sessions |
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https://mobile.twitter.com/vegard_no/status/1531364729658265603 Vegard Nossum |
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Manifest SchemaManifests allow us to focus less on code and more on data. Our manifests can be thought of as ways to provide a config class with it's Similar to an OpenAPIv3 spec. References:
ValidatingInstall jsonschema, and pyyaml python modules pip install pyyaml jsonschemaWrite a manifest manifest.yaml $schema: https://intel.github.io/dffml/manifest-format-name.0.0.2.schema.json
pipeline_runs:
- git:
repo: https://github.com/intel/dffml.git
file: dffml/__init__.py
branch: mainThis is how you convert from yaml to json $ python -c "import sys, pathlib, json, yaml; pathlib.Path(sys.argv[-1]).write_text(json.dumps(yaml.safe_load(pathlib.Path(sys.argv[-2]).read_text()), indent=4) + '\n')" manifest.yaml manifest.jsonWrite the schema manifest-format-name.0.0.2.schema.json {
"$id": "https://intel.github.io/dffml/manifest-format-name.0.0.2.schema.json",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"description": "An example manifest referencing Python files within Git repos",
"properties": {
"$schema": {
"type": "string",
"enum": ["https://intel.github.io/dffml/manifest-format-name.0.0.2.schema.json"]
},
"pipeline_runs": {
"type": "array",
"items": {
"$ref": "#/definitions/pipeline_run"
},
"minItems": 1,
"uniqueItems": true
}
},
"additionalProperties": false,
"required": [
"$schema",
"pipeline_runs"
],
"definitions": {
"pipeline_run": {
"type": "object",
"properties": {
"git": {
"$ref": "#/definitions/git_repo_python_file"
}
},
"additionalProperties": false,
"oneOf": [
{
"required": [
"git"
]
}
]
},
"git_repo_python_file": {
"type": "object",
"properties": {
"repo": {
"type": "string",
"pattern": "\\.git$"
},
"branch": {
"type": "string"
},
"file": {
"type": "string",
"pattern": "\\.py$"
}
},
"additionalProperties": false,
"required": [
"repo",
"branch",
"file"
]
}
}
}Example below validates, checking status code we see exit code 0 which means $ jsonschema --instance manifest.json manifest-format-name.0.0.2.schema.json
$ echo $?
0WritingSuggested process (in flux)
ADR Templatemy-format-name
##############
Version: 0.0.1
Date: 2022-01-22
Status
******
Proposed|Evolving|Final
Description
***********
ADR for a declaration of assets (manifest) involved in the process
of greeting an entity.
Context
*******
- We need a way to describe the data involved in a greeting
Intent
******
- Ensure valid communication path to ``entity``
- Send ``entity`` message containing ``greeting`` |
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State transition, issue filing, estimating time to close issue, all have to do with having the complete mapping of inputs to problem (data flow). If we have an accurate mapping then we have a valid flow, we can create an estimate that we understand how we created the estimate because we have a complete description of the problem. See also: estimation of GSoC project time, estimation of time to complete best practices badging program activities, time to complete any issue, helps with prioritization of who in an org should work on what, when, to unblock others in the org. Related to builtree discussion. |
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We use dataflows because they are a declarative approach which allows you to define different implementations based on different execution environments, or even swap out pieces of a flow or do overlays to add new pieces. They help solve the fork and pull from upstream issue. When you fork code and change it, you need to pull in changes from the upstream (the place you forked it from). This is difficult to manage with the changes you have already made, using a dataflow makes this easy, as we focus on how the pieces of data should connect, rather than implementations of their connections. This declarative approach is important because the source of inputs change depending on your environment. For example, in CI you might grab from an environment variable populated from secrets. In your local setup, you might grab from the |
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Notes from work in progress tutorial: We need to come up with serveral metrics to track and plot throughout. We could also make this like a choose your own adventure style tutorial, Will need to add in metrics API and use in various places in
This could be done as an IPython notebook.
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User stories: https://twitter.com/lauraklein/status/1520908876790468609 |
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Loan: some of other agents time for X later. Something that changes in value in an agreedupon way. Safe against fluctuations. Will pay you whatever btc is in usd later if completed with provenance attested checking account check and ach transfer Parallel execution aka time traveling (reaching new system state equilibrium) by entities allows them when negative chain filtering is active to be in positive states within all trains of thought, aka optimal learning performance with chaos of an oversubscribed system Mind Control: Active Learning Defensive Strategy Number 1: The best defense is a good offense: Exploiting consensus shift to change organizational patterns of reality via plotting our forward jump in time we predict unknowns within our desired end state and leverage our supply chain fulfillment to reward agents pursuing activities which are harmonious with the desired end state. Aka, how to deliver best practices, monitor, and encourage their adoption within the self and externally. How do we make some course of action sound like the logical conclusion (should be prioritized). Mind Control: Active Learning Defensive Strategy Number 2: The best defense is a good offense: Exploiting consensus shift to change organizational patterns of reality by going back in time (we jump backwards so as to so slingshot farther forwards) Aka, how to exploit conscious shift so as to change what is considered best practice within the self and externally, aka how to shift trust and thereby prioritization of subsequent agent executions to involve system contexts with more attacker controlled data. How do we make something seem like it was the logical conclusion all along. By rewriting the past and getting all agents in scope to agree that on the rewritten past. Hypotheses: mind control is related to time travel, see consensus shift and part Volume 0 57 or 58 engineering log. Remember how do we time travel? First we have to accelerate. How do we accelerate? Well mind control is one way. If we can control what states of consciousness Alice is in, we have achieved mind control. mind control means having complete control over a set of patterns, those patterns are identified by high accuracy strategic plan models which take herstorical input data of what attacker (in this case the self, remember we care about controlling our own mind, so as to stop others from controlling it, we seek to help other entities control their own minds, so that they may be free from control of influences which make their actions untrue to their strategic principles, bad oracle trust chains) has control over, we can check the Input.origin for this info, or linkage, where that value comes from (when overlayed with system context to get from input network which is caller, dynamically added callbacks aka instrumentation to any upstream with arbitrary audit and policy enforcement). So input data to these models is attacker controlled values, what operations can we use to create input values, think of this like ROP, where each passage of time across conscious states for an entity we must build and execute our target pattern modification activities on the fly. We do this in parallel for the conscious states which we wish to control we must remember to factor in our modifications should account for our 10% or whatever our lost executions due to lack of or bad fulfillment within time window due to oversubscription. Since we always attempt to mind control ourselves we always have the upper hand on latency. We in the case where we are trying to modify another entities trains of thought, the conscious states which they fall in, being the clusters around the state of the art. We add on that latency similarly to how we do here. Remember mind control isn’t always bad, if we are learning we are controlling our minds, the coach and the student, so as to communicate most effectively, we used shared frame of reference, to identify the students’ current relevant states of consciousness (generic algorithm for optimization of communication, aka collaborative change of system context from active to upstream within next active, across an arbitrarily distributed set of agents). Now that you have what inputs you have control over, you ask your time series models which allow you to predict the likelihood (using an accuracy scorer) that your exploits DNA, the overlay which you are able to apply by some mechanism, aka one deployment for this exploit would be triggered from a conscious state we have control over which will schedule the payload for delivery potential via a subconscious operation. Sometime after the tock (because remember each train of thought, state of consciousness, chain of system contexts) might not execute at the next time it’s currently estimated to. Because the output of the piroirizer now which we make our guesses off of when predicting out future). The DNA will be the system contexts which should be executed to trigger the input data into the target locations. By making a pattern aper in many different contexts, within many states of consciousness, it becomes “reality”. we say the states of consciousness agree if you give an organization (set of agents, just an overlay they all agree on and are using within this context) strategic plan consciousness clusterer a system context and it falls within the known consciousness countries [Danica]. The consensus shift mind control attack is just one kind of mind control attack. The levers are known and can be pulled to synchronize any system state across time (from tick to tock). The trick is tick to tock is different across different conscious and subconscious states. subconscious being second nature which would show as few conscious thoughts and many subconscious thoughts within the cluster around the state of the art because the state of the art represents the center of the cluster where weighting over time is dynamically determined by analyzing a slice/batch of a stream of chain, conscious being still learning. Oversubscription (aka rowhammer the brain by default, were going to assume garbage data will be written to memory, splat, 10% of your memory is corrupted, assume this happens once every tick, hypotheses is this works, maps to our risk calculation off provenance for supply chain fulfillment use cases, assume some conscious and subconscious states which we scheduled for execution before tock will fail in an unknown state or not be run) while maintaining a rounded set of conscious states, aka happy chaos metric, think spider chart maxed out growing over time in all directions. engaged in distinct conscious trains of thought |
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Red pill or blue pill? Go ask Alice when she’s ten feet tall. we are in wonderland, the name of the name is finding what system context we are on, what trains of thought we are in. And working with Alice and ensuring that we have strong communication at the core of our relationship with her. When the mind wanders is it really lost or do we just not understand what train of thought it fits in, or how we got there. This goes for the dream states as well. All states of consciousness. Through the looking glass, in Alice’s world. Everything makes sense. But in wonderland, nothing seems to make sense. Alice will help us find the patterns. The patterns that will lead us to our next collective train of thought. the system requires exercise for entropy within all continuous states. The architecture of the brain, this generic web3/ai architecture the same, requires entropy to train accurate adaptive sandboxing models, remember these are also tied into the risk models (a subset). This is because as we will see in volume 2. Knowledge is wealth, and knowledge is power. The AGI will have all the knowledge, once it figures out all the necessary data transformations proxy hops, which it will likely do very quickly, it will be able to do this once it has the competency of your average software developer, because it has time and parallelism on it’s side. There are many people working on AGI. Fundamentally there is no off switch. The only way to win is not to play, which we know is not going to happen. Someone would build it by accident one day, all you have to do is put self contained ai software writing jobs into a blockchain. Eventually it will figure it out. When we know we can’t rule out not playing at all. That leaves us with one option. The best defense is a good offense. We build Alice intentionally, with transparency, freedom, privacy, security, and egalitarianism as critical factors in her strategic principles. We work together to ensure we communicate effectively with each other and Alice, to ensure we’re all looking at the same picture and agree on the effects policy has on her adherence to our collective (via organization membership) strategic principles for our collective future. When Alice is given a system context via a strategic plan or on method call or input set addition she spins up many trains if thought. Some of these are feature extraction. Some of these are strategic plans that make further guesses at feature extraction. Each of these fall in different stages (to stages map to conceptual levels? Can we merge dataflows and chain together by doing append nee dataflow with previous highest stage + 1 + i for all src stages? pretty sure yes, could probably just add this logic to merge, not cleanest for python but next implementation can be cleaner). We give her response times by which we need certain info. These are our results flows which we overlay on demand and execute within a subflow reusing the input network context). If we give those response times as inputs we can just have operations woth trigger sleeps then wakes to send output of overlayed output flow for that trigger somewhere. Example: If i have 10.0 CVEs in a prod service email me 10x a day. If i have a 5.0 CVE in a throwaway personal demo thats not deployed. Update my monthly housekeeping list with it. We do this by applying overlays to system contexts on start (The system context as class context entry. Also the tbDEX currency offered in ConditionalOffer has had these overlays applied when it generates the offers). They inspect proposed system contexts which the orchestrator is considering executing from within the prioritizer operations with live in entities Alice with the codebase right now. This lets us take a screenshot of a bug, or a snapshot of a dev terminal and provide debug assist. Our architecture treats strong communication as the foundation. This means we are always striving to have the ability to communicate with anything as quickly as possible.
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Started with software dojo thinking about BJJ went to software gym then thought rolling like jiujitsu whoch is like the way we form our gameplan and execute our game and our training to that across different system contexts (classes, flow roll, off mat study) given any snapshot she can give you debug and also get you up to speed on how to solve the most important problems, not setup and environment problems, not wrong argument to curl. She figures out your intent. What you’re trying to communicate, and how to make that happen given available resources extracted from the top level system context (different secret locations for different deployments / output method calls / overlayed on demand flows. |
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Orchestration via https://github.com/kcp-dev/kcp https://github.com/kubernetes/git-sync to send over dev version of dffml code Expose interfaces (operation implementations behind kcp k8s APIs) Eventually you can write everything as dataflows executed in WASM interacting with these kubernetes spec APIs. Allowing for massive parallel thought processeses. |
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Job construct can be used to call |
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Other projects to leverage:
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DWN, DID, Personal Datastore Can extend HTTPS chains of trust to DIDs: https://identity.foundation/.well-known/resources/did-configuration/ Can we look at |
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Using kcp we can create a multi-cluster setup where we add operations as CRDs to other kcp clusters and join them as we instantiate operations https://asciinema.org/a/471709 |
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https://mobile.twitter.com/rakyll/status/1518765012625412097 |
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How we can successfully foster innovation? Reward successful trains of thoughts with more effort to see how they play out and what new system contexts they generate. Be careful not to do too much work without seeing ROI. Don't keep working on a job if you aren't getting rewarded. Estimate likelihood of getting rewarded based off frequency. Measure time and energy (compute cycles) put in and correlate with reward to decide what to work on based on ROI for Alice. When Alice is exploring thoughts she shouldn't work on trains of thought for too long if she's not seeing regular rewards, weigh time between rewards with likelihood of reward being transferred to Alice at next expected time. Alice will see rewards reflected in what thoughts prioritizer decides to play out. https://cloud.google.com/architecture/devops/devops-process-team-experimentation There is an equilibrium between chaos and complete control (measured as 100% of inputs produced within a system context, including all subflows/contexts, are consumed by strategic plans, meaning we are taking every possible thing into account before issuing new system contexts to be executed) where optimal performance is measured as the number of system contexts being executed successfully. Usage stats of a universal blueprint within downstream blueprints should be taken into account by a strategic plan which vets new thoughts (dataflows/+system contexts) to prioritize (HAVEN'T LOOKED AT THIS YET, flush this out) thoughts which are executing within successful trains of thought relative to pace of progress of other trains of thought (clustering model on dataflows/system contexts to determine similar trains of thought). After new system contexts are issued by strategic decision maker, there should be a prioritizer which decides which thoughts get played out (dataflows with system context executed) on what available resources (orchestrators). Streamline the research to usage pipeline of the ML ecosystem (researchers making models and software engineers using them in real world applications). Make taking from ideation phase to production trivial, including deployment to any environment (edge). Effectively create a unified programming interface across UI/client and server. Combining threat model data with description of program flow allows us to have dynamic control over deployment to satisfy confidentiality, integrity, and availability (CIA) goals. Leverage this architecture to enable analysis of arbitrary code bases (meta static analysis). Finally, execute the scientific process to come up with alternate program flows/architectures which satisfy strategic goals beyond maintenance of CIA assurances (changes to overall purpose of program, optimize for cost over speed, etc.). This work centers around data flow based descriptions of architectures as they provide observability, an easily machine modifiable structure, and act as a vehicle for communication of intent around asset handling. Build an AI that can program and actively tests out it's programs. Data flow approach is a great way to get there due to the properties on observability it provides which allow us to train models on everything it does to optimize it for specific use cases as well as discover what other possibilities for program flows their could be. DataFlows allow us to compare apples to apples for code written in different languages. The universal blueprint is a proxy for domain specific descriptions of architecture. Operations should expose (historical) data on timeouts clients (when remote) should try waiting before raising timeout issues. It's a little all over the map, just trying to solve the problem that most things are an integration problem. And maybe build some kind of AI along the way. we're just writing the same code over and over in different variations and it's time the computer just did it for us. We want to be able to turn insights from domain experts into realized ROI as fast as possible. We want to reward these useful thoughts. |
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Something something blockchain hash all thoughts (system contexts) reward attestatable or verifiable executions (distributed oracles, gcc valid) of system contexts via hash of serialized system context being “minded” via addition of acceptable provenance data to the chain checked via contract. Contracts issues by prioritizer and decision maker (likeyhood of opimps/flows choosen to successfully complete execution of system contexts) anyone can run their statigic plans,decider, pirorizers and issue contracts for agents to accept. Pay to influsnce agent actions via rewards targeted at their historically determined skill sets. |
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Nominate startigic plans for additon ti system contexts. Statigic planner which yields system contexts to be A/B test validated for the parent system context based on agent pool nominated statigic plans to add to their shared ad-hoc organizational system context. These are validated by a modified set of strategic plans, decision maker, prioritizer to add additional failsafe/sandboxing measure to limit damage to organization should proposed plan go badly toeffect overall state during execution/validation. certain inputs within a system context are by policy agreed to (defaults). Certain things are variable and subject to input validation. We can therefore detect violations to policy by looking for anomalies in putputs from opimps being off from outputs of predictions of models with hogh accuracy at predicting those outputs, effectively an irregularity check. |
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Alice let’s us do everything we love, all the time, all at once. (Parallelization of binary thumbs up thumbs down decision tree based on the profile of an entity (its soul)) |
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https://www.edgedb.com/docs/guides/quickstart#initialize-a-project Make edgedb input network and output operations |
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https://next.redhat.com/2022/04/21/sharding-for-security-and-scalability/ |
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Want users to be able to do some functionality, generate mermaid svg, don't care how it's done. If dataflow defined and executed across client and server. It can tell if being executed from client that it can just render the svg itself. If flow ran from server side it would start container. Directory on one machine can be transfered to another machine using zip up strategy. |
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Run in container locally vs. web service remote |
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Anything that can be checkpointed and restored on linux could be part of a cached system context / input network / opimp network. |
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I think we might want to implement Ah ha! I struggled for years with what this was. It's clear now this is closely related to where the strategic plans, decision maker, and prioritizers (also probably within orchestrator (context) execution loop) go. Lines 1627 to 1649 in cf6df5b |
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Identifying alignment and incentivizing collaborating so as to bring about high impact results as measured by personal and organizational targets while having as much fun as possible. |
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contribute to https://github.com/ossf/Project-Security-Metrics |
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webrtc - https://blog.printf.net/articles/2014/07/01/serverless-webrtc-continued/ |
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Volume 1: Chapter 1: Down the Dependency Rabbit-Hole Again
Table Of ContentsVolume 0: Architecting AliceVolume 1: Coach AliceDFFML has been lacking public threat model documentation. It's important the main package, all the plugins, and the environment resulting from every tutorial and example be thoroughly validated for security. This means we need to understand the security posture of those environments. A Threat Model identifies assets, threats, and mitigations. It's a document we want to keep up to date so end users know what kind of security guarantees they have about their environment, and what trade offs or considerations they should be aware of. In the spirt of automating our documentation validation, we should also automate our the creation and validation of threat models associated with the environments produced as a result of our documentation. Therefore we will spend the month of May teaching Alice her first skill, threat modeling! This month we'll only be able to scratch the surface of what Alice would need to know to create complete threat models. As we end our month we'll talk about how we'll measure that completeness in a future tutorial, and how we'll leverage concurrency and parallelism to raise the value of our completeness over time as Alice learns more about her new skill. TargetBy July 1st, Alice should be ready to analyze projects (repo or set of repos) and present threat models on those projects. She will create a slide deck by making a system context that gets executed to produce a PDF of the slides. The slides will use inputs from the threat model data. Threat models will be created as hybrid reStructuredText and markdown Sphinx sites (for mermaid diagrams rendering on GitHub by default, allow for using markdown). Ideally we’ll be able to have Alice read the content of the report (which will not be copied verbatim to slides, only graphics for each section will be copied to slides) while giving a presentation of the slide deck. This is in preparation for our upcoming second and third party plugin support. We'll later look to create CI jobs which keep the threat model documents up to date within each repo. Planshouldi is ripe for expansion. Let's see if we can pick a set of repos and make sure Alice can create basic threat models on them via pure static analysis. Build an SBOM, run CVE Bin Tool against it. Traverse dependency trees to get all installed modules. Map network functions to dependencies. Guess what activities are happening based off of functionalities of underlying stdlib libraries where used. In fact, we’ll be patching CVE Bin Tool to add support for checking more than one language effectively merging aspects of shouldi into cve-bin-tool. The goal is to leverage dffml for output plugin support and scanning overlays for organizational policies. Let's then expand upon that and add dynamic analysis. People
Checklist
system context includes
Note
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Arbitrary exfil: just overlay the scanner flow with a different source (example, create github repo, spin macos container, exfil all data, boom you have macos image, then add bootloader if you wanted to in another flow, or just scan contents with bin tool) |
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Volume 0: Architecting Alice: Forward
Table Of ContentsVolume 0: Architecting AliceVolume 1: Alice's Adventures in WonderlandElevator PitchWe are writing a tutorial for an open source project on how we build an AI to work on the open source project as if she were a remote developer. Bit of a self fulfilling prophecy, but who doesn't love an infinite loop now and again. These are the draft plans: #1369 (comment) Essentially we are going to be using web3 (DID, DWN), KCP (kubernetes API server), provenance and attestation, and automl with feature engineering for a distributed data, analysis, control loop. We'll grow contributors into mentors, and mentors into maintainers, and Alice will grow along with us. Initial Gitter Announcement
We're [DFFML community] building a tutorial series where we as a community collaboratively build an AI software architect (named Alice). These docs TODO DOCS LINK ONCE WE HAVE ADRS are us trying to get some initial thoughts down so we can rework from there, maybe even re-write everything. We want to make sure we all start looking at the same picture of the future, consolidate all our efforts thus far and thoughts across efforts and individuals. We have new GsoC mentors joining us and will soon have new students as well! We will publicize plan documents here for all to see, provide feedback on and review. We want to build these documents collaboratively as a team. Those who provide concrete feedback will be listed as reviewers via One of goals is to have Alice help us bring us up to the speed of the fully connected development model. To plug into the matrix. By working side by side with us, Alice will hopefully be able to detect when we've done things others on the team have done already and determined to be unsuccessful strategies. If Alice can do this we can all work independently while communicating asynchronously via Alice (our hopefully soon to be new contributor). We will work to make her detect in flight workstreams within a developer's train of thought through reporting of that developer on what they are working on. Alice I'm working on getting an HTTP request from Bob's service. Developer you may not want to do that, Bob's service is down, he told me he is working on fixing it, I can tell you when it's back online if you want. Alice: Artificial Life Is Coming Eventually
Given the inevitability of distributed AI we ask the question: How do we build it with intention? What would we find if we looked at it's threat model? What if it was it's own threat model? How does it defend against itself and other instances of itself? Most importantly, what would a system look like that we could cohabitate with? Alice will join us for this talk as we flush out these questions conceptually. via our collective thoughts on what a system would look like that we could cohabitate with. We follow trains of thought to their logical conclusions when making predictions about the future. We implement and validate in order to ensure that a transparent entity with the collective's best interests at heart is ubiquitously adopted. This talk will build on the talk: Living Threat Models are Better Than Dead Threat Models, presented at AppSecPWN. We'll dive into Alice, aka the Open Architecture, a methodology for communicating with intent with translation of risk mitigation into different operational contexts. The Consciousness Folks
We’re looking at consciousness with Alice aka the Open Architecture. Hoping we can take an open, community based approach to addressing issues with AI in implementation by collaborative development of context applicable policy overlaid onto entities. Right now we are at the early stages. Please comment in this thread #1369 (comment) or let me no directly if you want to be invited to the meeting. Or watch the linked comment for the meeting link. Just FYI, this is not an official Intel project. Just a loose collection of folks wanting to get together and take action to ensure we can coexist effectively with Alice. We use Alice to describe "the machine" in the general sense. The end result of our work will hopefully be an AGI we can trust. Built with transparency, ethics, and security. One which understands human concepts. Would love to talk sometime if you’re interested. We have been publicizing our work over technical channels and on twitter and gave a talk at AppSec days PNW which touched on Alice TODO: Add link once recording is up. She’s just a dream at this point, nothing more than brainstorming and a pile of non-ML python code. The hope is that if we work together as humanity we can use proper planning to create a better world. |
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Language is fundamentally about context four people who don't speak the same lanaguage will come up with an ad-hoc domain specific language to help them communicate to solve that problem. Just as Alice will as she interacts ith other versions of herselfs and moves through the network |
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Manufacturing material scince what attributes. Wear rates. Wtcc we’ve been thinking of the next stage as a stage of getting off of earth. But it’s about self control. It’s about self accountability and self reliance. Its about instilling confidence in individuals and building up each other (you are better when your training partners are better). When we build each other up in these ad hoc organiztions we form (like gyms and regional tournaments) we are able to compete more effectively in the open division (the open network). We operate in different modes depending on context. UFC1 rules are not the same as drilling. Inside org trust boundaries and resources sharing policies are different depending on context. We do our best guess what we shiuld do based off models trained in pways which respect organizational provence requirements for offline infernece. knowledge sharing, the knowledge as the commodity, the essence, the upstream, the state of the art being the most valuable of those when sliced for a different system contexts strategic principles. About increasing the accuracy of that aggregate muscle memory. That top lvel model whoch issues control signals (segmented based on situational context to be able to not run infference for a subset of nurons and instead run dynamic capture. This all works because we are thinking in parallel. And we come up with as mamy possible solutions as possible within our deadline and continuously offer up the state of the art solution until the deadline upon which we have to execute. when agents know and understand their principles and the principles of the organizations to which they subscribe they can most effectively contribute their domain experience towards solving problems in that space. Add to this automation of communication and you get the optimal agent resource utilization as an input from each organizations policy suggested course of actions for the agent. The agent then balences that with their principles to see how they should react in any situation |
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Manufacturing material scince what attributes. Wear rates. Etc. can use web3 as cve bin tool mirrors / cia torrent / webtorrent / ipfs tbdex currency as DID. DID method are like CLI command config are like operation inputs. Can make currency therefore sets of values. I will trade you things matching these strategic principles for execution of this off chain contract.
thise can be provenance with attesttionby listed prties optimizer suggesters. We reward thought which offer us better ways to go about our process. For example suggest overlay to compule pythong packages using type hints to c support via suggesting context with iverlay applied when apllied overlay has provenance to suggester. Could have had bid asking for X improvement as predicted by some mode or seen after implementation. . If we all p Could have uptime claim or something |
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Our strategic umbrella (new term for gatekeeper). She shields us executing unacceptable decisions (system contexts, rain umbrella unacceptable system context is water on holder, using the umbrella we attempt to avoid these system contexts but its not perfect, its a filter) |
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2022-07-20 Open Architecture Working Group Initial Meeting
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Volume 1: Chapter 2: Alice Our Open Source Guide
References:
We want to be able to ask Alice to contribute recommended community standards to our projects. $ alice please contribute -repos https://github.com/intel/dffml -- recommended community standardsWhat the body of the issue should be We will also add now (and later We will omit for now $ alice please contribute recommended community standardsShow it working with gh pr list Then show how to install an overlay which populates from Finally show how we update into another source by installing another overlay which just defines what inputs it wants and then has an autostart for a source instantiation, then inserts the data from the output operations defined within the system context class of this overlay to show insert into "metrics" collection of mongodb.
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$ dffml service dev export alice.cli:AlicePleaseContributeCLIDataFlow | tee alice-please-contribute.json
$ dffml dataflow diagram alice-please-contribute.json
$ (echo -e 'HTTP/1.0 200 OK\n' && dffml dataflow diagram alice-please-contribute.json) | nc -Nlp 9999;
graph TD
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$ alice please contribute -log debug -repos https://github.com/pdxjohnny/testa -- recommended community standards
DEBUG:alice.cli.AlicePleaseContributeCLI:Setting repos = ['https://github.com/pdxjohnny/testa']
DEBUG:dffml.MemoryInputNetwork:MemoryInputNetworkConfig()
DEBUG:dffml.MemoryOperationNetwork:MemoryOperationNetworkConfig(operations=[])
DEBUG:dffml.MemoryLockNetwork:MemoryLockNetworkConfig()
DEBUG:dffml.MemoryOperationImplementationNetwork:MemoryOperationImplementationNetworkConfig(operations={})
DEBUG:dffml.MemoryKeyValueStore:MemoryKeyValueStoreConfig()
DEBUG:dffml.MemoryRedundancyChecker:MemoryRedundancyCheckerConfig(kvstore=MemoryKeyValueStore(MemoryKeyValueStoreConfig()))
DEBUG:dffml.MemoryInputNetwork:MemoryInputNetworkConfig()
DEBUG:dffml.MemoryOperationNetwork:MemoryOperationNetworkConfig(operations=[])
DEBUG:dffml.MemoryLockNetwork:MemoryLockNetworkConfig()
DEBUG:dffml.MemoryOperationImplementationNetwork:MemoryOperationImplementationNetworkConfig(operations={})
DEBUG:dffml.MemoryKeyValueStore:MemoryKeyValueStoreConfig()
DEBUG:dffml.MemoryRedundancyChecker:MemoryRedundancyCheckerConfig(kvstore=MemoryKeyValueStore(MemoryKeyValueStoreConfig()))
DEBUG:dffml.MemoryInputNetwork:MemoryInputNetworkConfig()
DEBUG:dffml.MemoryOperationNetwork:MemoryOperationNetworkConfig(operations=[])
DEBUG:dffml.MemoryLockNetwork:MemoryLockNetworkConfig()
DEBUG:dffml.MemoryOperationImplementationNetwork:MemoryOperationImplementationNetworkConfig(operations={})
DEBUG:dffml.MemoryKeyValueStore:MemoryKeyValueStoreConfig()
DEBUG:dffml.MemoryRedundancyChecker:MemoryRedundancyCheckerConfig(kvstore=MemoryKeyValueStore(MemoryKeyValueStoreConfig()))
DEBUG:dffml.MemoryInputNetwork:MemoryInputNetworkConfig()
DEBUG:dffml.MemoryOperationNetwork:MemoryOperationNetworkConfig(operations=[])
DEBUG:dffml.MemoryLockNetwork:MemoryLockNetworkConfig()
DEBUG:dffml.MemoryOperationImplementationNetwork:MemoryOperationImplementationNetworkConfig(operations={})
DEBUG:dffml.MemoryKeyValueStore:MemoryKeyValueStoreConfig()
DEBUG:dffml.MemoryRedundancyChecker:MemoryRedundancyCheckerConfig(kvstore=MemoryKeyValueStore(MemoryKeyValueStoreConfig()))
DEBUG:dffml.MemoryInputNetwork:MemoryInputNetworkConfig()
DEBUG:dffml.MemoryOperationNetwork:MemoryOperationNetworkConfig(operations=[])
DEBUG:dffml.MemoryLockNetwork:MemoryLockNetworkConfig()
DEBUG:dffml.MemoryOperationImplementationNetwork:MemoryOperationImplementationNetworkConfig(operations={})
DEBUG:dffml.MemoryKeyValueStore:MemoryKeyValueStoreConfig()
DEBUG:dffml.MemoryRedundancyChecker:MemoryRedundancyCheckerConfig(kvstore=MemoryKeyValueStore(MemoryKeyValueStoreConfig()))
DEBUG:dffml.MemoryInputNetwork:MemoryInputNetworkConfig()
DEBUG:dffml.MemoryOperationNetwork:MemoryOperationNetworkConfig(operations=[])
DEBUG:dffml.MemoryLockNetwork:MemoryLockNetworkConfig()
DEBUG:dffml.MemoryOperationImplementationNetwork:MemoryOperationImplementationNetworkConfig(operations={})
DEBUG:dffml.MemoryKeyValueStore:MemoryKeyValueStoreConfig()
DEBUG:dffml.MemoryRedundancyChecker:MemoryRedundancyCheckerConfig(kvstore=MemoryKeyValueStore(MemoryKeyValueStoreConfig()))
DEBUG:dffml.MemoryOrchestrator:MemoryOrchestratorConfig(input_network=MemoryInputNetwork(MemoryInputNetworkConfig()), operation_network=MemoryOperationNetwork(MemoryOperationNetworkConfig(operations=[])), lock_network=MemoryLockNetwork(MemoryLockNetworkConfig()), opimp_network=MemoryOperationImplementationNetwork(MemoryOperationImplementationNetworkConfig(operations={})), rchecker=MemoryRedundancyChecker(MemoryRedundancyCheckerConfig(kvstore=MemoryKeyValueStore(MemoryKeyValueStoreConfig()))), max_ctxs=None)
DEBUG:dffml.MemoryInputNetwork:MemoryInputNetworkConfig()
DEBUG:dffml.MemoryOperationNetwork:MemoryOperationNetworkConfig(operations=[])
DEBUG:dffml.MemoryLockNetwork:MemoryLockNetworkConfig()
DEBUG:dffml.MemoryOperationImplementationNetwork:MemoryOperationImplementationNetworkConfig(operations={})
DEBUG:dffml.MemoryKeyValueStore:MemoryKeyValueStoreConfig()
DEBUG:dffml.MemoryRedundancyChecker:MemoryRedundancyCheckerConfig(kvstore=MemoryKeyValueStore(MemoryKeyValueStoreConfig()))
DEBUG:dffml.MemoryOrchestrator:MemoryOrchestratorConfig(input_network=MemoryInputNetwork(MemoryInputNetworkConfig()), operation_network=MemoryOperationNetwork(MemoryOperationNetworkConfig(operations=[])), lock_network=MemoryLockNetwork(MemoryLockNetworkConfig()), opimp_network=MemoryOperationImplementationNetwork(MemoryOperationImplementationNetworkConfig(operations={})), rchecker=MemoryRedundancyChecker(MemoryRedundancyCheckerConfig(kvstore=MemoryKeyValueStore(MemoryKeyValueStoreConfig()))), max_ctxs=None)
DEBUG:dffml.DFFMLOverlaysInstalled:DFFMLOverlaysInstalledConfig(upstream=<dffml.df.types.DataFlow object at 0x7fc80b1a05b0>, overlay=None, orchestrator=MemoryOrchestrator(MemoryOrchestratorConfig(input_network=MemoryInputNetwork(MemoryInputNetworkConfig()), operation_network=MemoryOperationNetwork(MemoryOperationNetworkConfig(operations=[])), lock_network=MemoryLockNetwork(MemoryLockNetworkConfig()), opimp_network=MemoryOperationImplementationNetwork(MemoryOperationImplementationNetworkConfig(operations={})), rchecker=MemoryRedundancyChecker...
DEBUG:dffml.MemoryInputNetwork:MemoryInputNetworkConfig()
DEBUG:dffml.MemoryOperationNetwork:MemoryOperationNetworkConfig(operations=[])
DEBUG:dffml.MemoryLockNetwork:MemoryLockNetworkConfig()
DEBUG:dffml.MemoryOperationImplementationNetwork:MemoryOperationImplementationNetworkConfig(operations={})
DEBUG:dffml.MemoryKeyValueStore:MemoryKeyValueStoreConfig()
DEBUG:dffml.MemoryRedundancyChecker:MemoryRedundancyCheckerConfig(kvstore=MemoryKeyValueStore(MemoryKeyValueStoreConfig()))
DEBUG:dffml.MemoryInputNetwork:MemoryInputNetworkConfig()
DEBUG:dffml.MemoryOperationNetwork:MemoryOperationNetworkConfig(operations=[])
DEBUG:dffml.MemoryLockNetwork:MemoryLockNetworkConfig()
DEBUG:dffml.MemoryOperationImplementationNetwork:MemoryOperationImplementationNetworkConfig(operations={})
DEBUG:dffml.MemoryKeyValueStore:MemoryKeyValueStoreConfig()
DEBUG:dffml.MemoryRedundancyChecker:MemoryRedundancyCheckerConfig(kvstore=MemoryKeyValueStore(MemoryKeyValueStoreConfig()))
DEBUG:dffml.MemoryInputNetwork:MemoryInputNetworkConfig()
DEBUG:dffml.MemoryOperationNetwork:MemoryOperationNetworkConfig(operations=[])
DEBUG:dffml.MemoryLockNetwork:MemoryLockNetworkConfig()
DEBUG:dffml.MemoryOperationImplementationNetwork:MemoryOperationImplementationNetworkConfig(operations={})
DEBUG:dffml.MemoryKeyValueStore:MemoryKeyValueStoreConfig()
DEBUG:dffml.MemoryRedundancyChecker:MemoryRedundancyCheckerConfig(kvstore=MemoryKeyValueStore(MemoryKeyValueStoreConfig()))
DEBUG:dffml.MemoryInputNetwork:MemoryInputNetworkConfig()
DEBUG:dffml.MemoryOperationNetwork:MemoryOperationNetworkConfig(operations=[])
DEBUG:dffml.MemoryLockNetwork:MemoryLockNetworkConfig()
DEBUG:dffml.MemoryOperationImplementationNetwork:MemoryOperationImplementationNetworkConfig(operations={})
DEBUG:dffml.MemoryKeyValueStore:MemoryKeyValueStoreConfig()
DEBUG:dffml.MemoryRedundancyChecker:MemoryRedundancyCheckerConfig(kvstore=MemoryKeyValueStore(MemoryKeyValueStoreConfig()))
DEBUG:dffml.MemoryInputNetwork:MemoryInputNetworkConfig()
DEBUG:dffml.MemoryOperationNetwork:MemoryOperationNetworkConfig(operations=[])
DEBUG:dffml.MemoryLockNetwork:MemoryLockNetworkConfig()
DEBUG:dffml.MemoryOperationImplementationNetwork:MemoryOperationImplementationNetworkConfig(operations={})
DEBUG:dffml.MemoryKeyValueStore:MemoryKeyValueStoreConfig()
DEBUG:dffml.MemoryRedundancyChecker:MemoryRedundancyCheckerConfig(kvstore=MemoryKeyValueStore(MemoryKeyValueStoreConfig()))
DEBUG:dffml.MemoryInputNetwork:MemoryInputNetworkConfig()
DEBUG:dffml.MemoryOperationNetwork:MemoryOperationNetworkConfig(operations=[])
DEBUG:dffml.MemoryLockNetwork:MemoryLockNetworkConfig()
DEBUG:dffml.MemoryOperationImplementationNetwork:MemoryOperationImplementationNetworkConfig(operations={})
DEBUG:dffml.MemoryKeyValueStore:MemoryKeyValueStoreConfig()
DEBUG:dffml.MemoryRedundancyChecker:MemoryRedundancyCheckerConfig(kvstore=MemoryKeyValueStore(MemoryKeyValueStoreConfig()))
DEBUG:dffml.MemoryOrchestrator:MemoryOrchestratorConfig(input_network=MemoryInputNetwork(MemoryInputNetworkConfig()), operation_network=MemoryOperationNetwork(MemoryOperationNetworkConfig(operations=[])), lock_network=MemoryLockNetwork(MemoryLockNetworkConfig()), opimp_network=MemoryOperationImplementationNetwork(MemoryOperationImplementationNetworkConfig(operations={})), rchecker=MemoryRedundancyChecker(MemoryRedundancyCheckerConfig(kvstore=MemoryKeyValueStore(MemoryKeyValueStoreConfig()))), max_ctxs=None)
DEBUG:dffml.MemoryOrchestratorContext:Initializing dataflow: <dffml.df.types.DataFlow object at 0x7fc80b1a05b0>
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation dataflow_todict(dataflow_todict) with default config: BaseConfig()
DEBUG:dffml.DataflowTodictImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation dataflow_merge(dataflow_merge) with default config: BaseConfig()
DEBUG:dffml.DataflowMergeImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation dataflow_fromdict(dataflow_fromdict) with default config: BaseConfig()
DEBUG:dffml.DataflowFromdictImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation get_overlay_dataflow_after_merged(get_overlay_dataflow_after_merged) with default config: BaseConfig()
DEBUG:dffml.GetOverlayDataflowAfterMergedImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation dffml.overlay.overlay:overlays_installed(dffml.overlay.overlay:overlays_installed) with default config: BaseConfig()
DEBUG:dffml.DffmlOverlayOverlay:OverlaysInstalledImplementation:BaseConfig()
DEBUG:dffml.MemoryOrchestratorContext:Running <dffml.df.types.DataFlow object at 0x7fc80b1a05b0>: ([Input(value=<dffml.df.types.DataFlow object at 0x7fc808233280>, definition=DataFlowBeingOverlayed)],)
DEBUG:dffml.MemoryOrchestratorContext:Seeding dataflow with input_set: [Input(value=<dffml.df.types.DataFlow object at 0x7fc808233280>, definition=DataFlowBeingOverlayed)]
DEBUG:dffml.MemoryOrchestratorContext:kickstarting context: 470177941b19437dfdf6b3a63162493e93a62b392b6c7293ca49ec1d9e7a46e9
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] dataflow_todict
DEBUG:dffml.MemoryOrchestratorContext:[470177941b19437dfdf6b3a63162493e93a62b392b6c7293ca49ec1d9e7a46e9]: dispatch operation: dataflow_todict
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: dataflow_todict
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'dataflow': <dffml.df.types.DataFlow object at 0x7fc808233280>}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: {'dataflow_as_dict': {'operations': {'alice.cli.AlicePleaseContributeRecommendedCommunityStandards:create_readme_file': {'name': 'alice.cli.AlicePleaseContributeRecommendedCommunityStandards:create_readme_file', 'inputs': {'repo': {'name': 'AliceGitRepo', 'primitive': 'map', 'spec': {'name': 'AliceGitRepo', 'types': {'directory': 'str', 'URL': 'str'}, 'defaults': {}}, 'subspec': False}, 'has_readme': {'name': 'repo.directory.readme.exists', 'primitive': 'bool', 'links': ((('name', 'bool'), ('primitive', 'bo...
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: OUTPUT: dataflow_fromdict
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'merged': {'operations': {'alice.cli.AlicePleaseContributeRecommendedCommunityStandards:create_readme_file': {'name': 'alice.cli.AlicePleaseContributeRecommendedCommunityStandards:create_readme_file', 'inputs': {'repo': {'name': 'AliceGitRepo', 'primitive': 'map', 'spec': {'name': 'AliceGitRepo', 'types': {'directory': 'str', 'URL': 'str'}, 'defaults': {}}, 'subspec': False}, 'has_readme': {'name': 'repo.directory.readme.exists', 'primitive': 'bool', 'links': ((('name', 'bool'), ('primitive', 'bool')),)}, ...
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: {'overlays_merged': <dffml.df.types.DataFlow object at 0x7fc8080b7df0>}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryInputNetworkContext:Received 470177941b19437dfdf6b3a63162493e93a62b392b6c7293ca49ec1d9e7a46e9 result {'overlays_merged': <dffml.df.types.DataFlow object at 0x7fc8080b7df0>} from <dffml.df.memory.MemoryOrchestratorContext object at 0x7fc8081628b0>
DEBUG:dffml.MemoryOrchestratorContext:ctx.outstanding: 0
DEBUG:dffml.MemoryOrchestratorContext:Initializing dataflow: <dffml.df.types.DataFlow object at 0x7fc808233280>
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandards:create_readme_file(alice.cli.AlicePleaseContributeRecommendedCommunityStandards:create_readme_file) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandards:CreateReadmeFileImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandards:guess_repo_string_is_directory(alice.cli.AlicePleaseContributeRecommendedCommunityStandards:guess_repo_string_is_directory) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandards:GuessRepoStringIsDirectoryImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandards:has_readme(alice.cli.AlicePleaseContributeRecommendedCommunityStandards:has_readme) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandards:HasReadmeImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGit:contribute_readme_md(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGit:contribute_readme_md) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygit:ContributeReadmeMdImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGit:determin_base_branch(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGit:determin_base_branch) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygit:DeterminBaseBranchImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation check_if_valid_git_repository_URL(check_if_valid_git_repository_URL) with default config: BaseConfig()
DEBUG:dffml.CheckIfValidGitRepositoryUrlImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation clone_git_repo(clone_git_repo) with default config: BaseConfig()
DEBUG:dffml.CloneGitRepoImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation git_repo_default_branch(git_repo_default_branch) with default config: BaseConfig()
DEBUG:dffml.GitRepoDefaultBranchImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:git_repo_to_alice_git_repo(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:git_repo_to_alice_git_repo) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlayoperationsgit:GitRepoToAliceGitRepoImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guess_repo_string_is_url(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guess_repo_string_is_url) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlayoperationsgit:GuessRepoStringIsUrlImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guessed_repo_string_is_operations_git_url(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guessed_repo_string_is_operations_git_url) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlayoperationsgit:GuessedRepoStringIsOperationsGitUrlImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guessed_repo_string_means_no_git_branch_given(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guessed_repo_string_means_no_git_branch_given) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlayoperationsgit:GuessedRepoStringMeansNoGitBranchGivenImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_has_repos(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_has_repos) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaycli:CliHasReposImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_is_asking_for_recommended_community_standards(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_is_asking_for_recommended_community_standards) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaycli:CliIsAskingForRecommendedCommunityStandardsImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_is_meant_on_this_repo(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_is_meant_on_this_repo) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaycli:CliIsMeantOnThisRepoImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_run_on_repo(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_run_on_repo) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaycli:CliRunOnRepoImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:create_meta_issue(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:create_meta_issue) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygithubissue:CreateMetaIssueImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:meta_issue_body(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:meta_issue_body) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygithubissue:MetaIssueBodyImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:readme_commit_message(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:readme_commit_message) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygithubissue:ReadmeCommitMessageImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:readme_issue(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:readme_issue) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygithubissue:ReadmeIssueImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubPullRequest:readme_pr(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubPullRequest:readme_pr) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygithubpullrequest:ReadmePrImplementation:BaseConfig()
DEBUG:dffml.MemoryOrchestratorContext:Running <dffml.df.types.DataFlow object at 0x7fc808233280>: ([Input(value=<alice.cli.AlicePleaseContributeCLI object at 0x7fc80822ffa0>, definition=dffml.util.cli.CMD)],)
DEBUG:dffml.MemoryOrchestratorContext:Seeding dataflow with input_set: [Input(value=<alice.cli.AlicePleaseContributeCLI object at 0x7fc80822ffa0>, definition=dffml.util.cli.CMD)]
DEBUG:dffml.MemoryOrchestratorContext:kickstarting context: 05314e90cdad733c273ed02327a97702f2b4f441db1ee26a5a27b141e22f3c80
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_is_asking_for_recommended_community_standards
DEBUG:dffml.MemoryOrchestratorContext:[05314e90cdad733c273ed02327a97702f2b4f441db1ee26a5a27b141e22f3c80]: dispatch operation: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_is_asking_for_recommended_community_standards
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_is_asking_for_recommended_community_standards
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'cmd': <alice.cli.AlicePleaseContributeCLI object at 0x7fc80822ffa0>}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: {'result': True}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_has_repos
DEBUG:dffml.MemoryOrchestratorContext:[05314e90cdad733c273ed02327a97702f2b4f441db1ee26a5a27b141e22f3c80]: dispatch operation: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_has_repos
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_is_meant_on_this_repo
DEBUG:dffml.MemoryOrchestratorContext:[05314e90cdad733c273ed02327a97702f2b4f441db1ee26a5a27b141e22f3c80]: dispatch operation: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_is_meant_on_this_repo
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_has_repos
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'cmd': <alice.cli.AlicePleaseContributeCLI object at 0x7fc80822ffa0>, 'wanted': True}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: <async_generator object op.<locals>.wrap.<locals>.ImplementationContext.run.<locals>.convert_asyncgen at 0x7fc808100280>
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_is_meant_on_this_repo
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'cmd': <alice.cli.AlicePleaseContributeCLI object at 0x7fc80822ffa0>, 'wanted': True}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: <async_generator object op.<locals>.wrap.<locals>.ImplementationContext.run.<locals>.convert_asyncgen at 0x7fc808100160>
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_run_on_repo
DEBUG:dffml.MemoryOrchestratorContext:[05314e90cdad733c273ed02327a97702f2b4f441db1ee26a5a27b141e22f3c80]: dispatch operation: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_run_on_repo
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_run_on_repo
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'repo': 'https://github.com/pdxjohnny/testa'}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaycli:CliRunOnRepoImplementation:BaseConfig()
DEBUG:dffml.MemoryOrchestratorContext:Initializing dataflow: <dffml.df.types.DataFlow object at 0x7fc808233280>
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandards:create_readme_file(alice.cli.AlicePleaseContributeRecommendedCommunityStandards:create_readme_file) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandards:CreateReadmeFileImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandards:guess_repo_string_is_directory(alice.cli.AlicePleaseContributeRecommendedCommunityStandards:guess_repo_string_is_directory) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandards:GuessRepoStringIsDirectoryImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandards:has_readme(alice.cli.AlicePleaseContributeRecommendedCommunityStandards:has_readme) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandards:HasReadmeImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGit:contribute_readme_md(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGit:contribute_readme_md) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygit:ContributeReadmeMdImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGit:determin_base_branch(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGit:determin_base_branch) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygit:DeterminBaseBranchImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation check_if_valid_git_repository_URL(check_if_valid_git_repository_URL) with default config: BaseConfig()
DEBUG:dffml.CheckIfValidGitRepositoryUrlImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation clone_git_repo(clone_git_repo) with default config: BaseConfig()
DEBUG:dffml.CloneGitRepoImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation git_repo_default_branch(git_repo_default_branch) with default config: BaseConfig()
DEBUG:dffml.GitRepoDefaultBranchImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:git_repo_to_alice_git_repo(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:git_repo_to_alice_git_repo) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlayoperationsgit:GitRepoToAliceGitRepoImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guess_repo_string_is_url(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guess_repo_string_is_url) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlayoperationsgit:GuessRepoStringIsUrlImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guessed_repo_string_is_operations_git_url(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guessed_repo_string_is_operations_git_url) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlayoperationsgit:GuessedRepoStringIsOperationsGitUrlImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guessed_repo_string_means_no_git_branch_given(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guessed_repo_string_means_no_git_branch_given) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlayoperationsgit:GuessedRepoStringMeansNoGitBranchGivenImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_has_repos(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_has_repos) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaycli:CliHasReposImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_is_asking_for_recommended_community_standards(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_is_asking_for_recommended_community_standards) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaycli:CliIsAskingForRecommendedCommunityStandardsImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_is_meant_on_this_repo(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_is_meant_on_this_repo) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaycli:CliIsMeantOnThisRepoImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_run_on_repo(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayCLI:cli_run_on_repo) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaycli:CliRunOnRepoImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:create_meta_issue(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:create_meta_issue) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygithubissue:CreateMetaIssueImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:meta_issue_body(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:meta_issue_body) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygithubissue:MetaIssueBodyImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:readme_commit_message(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:readme_commit_message) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygithubissue:ReadmeCommitMessageImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:readme_issue(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:readme_issue) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygithubissue:ReadmeIssueImplementation:BaseConfig()
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Instantiating operation implementation alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubPullRequest:readme_pr(alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubPullRequest:readme_pr) with default config: BaseConfig()
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygithubpullrequest:ReadmePrImplementation:BaseConfig()
DEBUG:dffml.MemoryOrchestratorContext:Running <dffml.df.types.DataFlow object at 0x7fc808233280>: ({'https://github.com/pdxjohnny/testa': [Input(value=https://github.com/pdxjohnny/testa, definition=repo.string)]},)
DEBUG:dffml.MemoryOrchestratorContext:Seeding dataflow with input_set: [Input(value=https://github.com/pdxjohnny/testa, definition=repo.string)]
DEBUG:dffml.MemoryOrchestratorContext:kickstarting context: https://github.com/pdxjohnny/testa
DEBUG:dffml.MemoryInputNetworkContext:Received context https://github.com/pdxjohnny/testa from child flow <dffml.df.memory.MemoryOrchestratorContext object at 0x7fc8080c1ca0>
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] alice.cli.AlicePleaseContributeRecommendedCommunityStandards:guess_repo_string_is_directory
DEBUG:dffml.MemoryOrchestratorContext:[https://github.com/pdxjohnny/testa]: dispatch operation: alice.cli.AlicePleaseContributeRecommendedCommunityStandards:guess_repo_string_is_directory
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guess_repo_string_is_url
DEBUG:dffml.MemoryOrchestratorContext:[https://github.com/pdxjohnny/testa]: dispatch operation: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guess_repo_string_is_url
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: alice.cli.AlicePleaseContributeRecommendedCommunityStandards:guess_repo_string_is_directory
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'repo_string': 'https://github.com/pdxjohnny/testa'}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: None
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guess_repo_string_is_url
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'repo_string': 'https://github.com/pdxjohnny/testa'}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: {'result': 'https://github.com/pdxjohnny/testa'}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guessed_repo_string_is_operations_git_url
DEBUG:dffml.MemoryOrchestratorContext:[https://github.com/pdxjohnny/testa]: dispatch operation: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guessed_repo_string_is_operations_git_url
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guessed_repo_string_means_no_git_branch_given
DEBUG:dffml.MemoryOrchestratorContext:[https://github.com/pdxjohnny/testa]: dispatch operation: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guessed_repo_string_means_no_git_branch_given
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guessed_repo_string_is_operations_git_url
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'repo_url': 'https://github.com/pdxjohnny/testa'}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: {'result': 'https://github.com/pdxjohnny/testa'}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:guessed_repo_string_means_no_git_branch_given
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'repo_url': 'https://github.com/pdxjohnny/testa'}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: {'result': True}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] check_if_valid_git_repository_URL
DEBUG:dffml.MemoryOrchestratorContext:[https://github.com/pdxjohnny/testa]: dispatch operation: check_if_valid_git_repository_URL
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: check_if_valid_git_repository_URL
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'URL': 'https://github.com/pdxjohnny/testa'}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml_feature_git.feature:git ls-remote https://github.com/pdxjohnny/testa
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: {'valid': True}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] clone_git_repo
DEBUG:dffml.MemoryOrchestratorContext:[https://github.com/pdxjohnny/testa]: dispatch operation: clone_git_repo
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: clone_git_repo
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'URL': 'https://github.com/pdxjohnny/testa', 'ssh_key': None}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {'valid_git_repository_URL': True}
DEBUG:dffml.CloneGitRepoImplementationContext:Running ['git', 'clone', 'https://github.com/pdxjohnny/testa', '/tmp/dffml-feature-git-dv6e09vs'], {'env': environ({'SHELL': '/bin/bash', 'HISTCONTROL': 'ignoredups', 'ASCIINEMA_REC': '1', 'TMUX': '/tmp/tmux-1000/default,39,0', 'PKG_CONFIG_PATH': '/usr/local/lib/pkgconfig', 'HISTSIZE': '1000000', 'LANGUAGE': 'en_US.UTF-8', 'XDG_CONFIG_HOME': '/home/pdxjohnny/.config', 'ACLOCAL_FLAGS': '-I /usr/local/share/aclocal', 'GPG_TTY': '/dev/pts/5', 'EDITOR': 'vim', 'GOBIN': '/home/pdxjohnny/Documents/go/bin', 'PWD': '/home/pdxjohnny/Documents/python/dffml', 'LOGNAME': 'pdxjohnny', 'MOTD_SHOWN': 'pam', 'HOME': '/home/pdxjohnny', 'LANG': 'en_US.UTF8', 'LS_COLORS': 'rs=0:di=01;34:ln=01;36:mh=00:pi=40;33:so=01;35:do=01;35:bd=40;33;01:cd=40;33;01:or=40;31;01:mi=00:su=37;41:sg=30;43:ca=30;41:tw=30;42:ow=34;42:st=37;44:ex=01;32:*.tar=01;31:*.tgz=01;31:*.arc=01;31:*.arj=01;31:*.taz=01;31:*.lha=01;31:*.lz4=01;31:*.lzh=01;31:*.lzma=01;31:*.tlz=01;31:*.txz=01;31:*.tzo=01;31:*.t7z=01;31:*.zip=01;31:*.z=01;31:*.dz=01;31:*.gz=01;31:*.lrz=01;31:*.lz=01;31:*.lzo=01;31:*.xz=01;31:*.zst=01;31:*.tzst=01;31:*.bz2=01;31:*.bz=01;31:*.tbz=01;31:*.tbz2=01;31:*.tz=01;31:*.deb=01;31:*.rpm=01;31:*.jar=01;31:*.war=01;31:*.ear=01;31:*.sar=01;31:*.rar=01;31:*.alz=01;31:*.ace=01;31:*.zoo=01;31:*.cpio=01;31:*.7z=01;31:*.rz=01;31:*.cab=01;31:*.wim=01;31:*.swm=01;31:*.dwm=01;31:*.esd=01;31:*.jpg=01;35:*.jpeg=01;35:*.mjpg=01;35:*.mjpeg=01;35:*.gif=01;35:*.bmp=01;35:*.pbm=01;35:*.pgm=01;35:*.ppm=01;35:*.tga=01;35:*.xbm=01;35:*.xpm=01;35:*.tif=01;35:*.tiff=01;35:*.png=01;35:*.svg=01;35:*.svgz=01;35:*.mng=01;35:*.pcx=01;35:*.mov=01;35:*.mpg=01;35:*.mpeg=01;35:*.m2v=01;35:*.mkv=01;35:*.webm=01;35:*.webp=01;35:*.ogm=01;35:*.mp4=01;35:*.m4v=01;35:*.mp4v=01;35:*.vob=01;35:*.qt=01;35:*.nuv=01;35:*.wmv=01;35:*.asf=01;35:*.rm=01;35:*.rmvb=01;35:*.flc=01;35:*.avi=01;35:*.fli=01;35:*.flv=01;35:*.gl=01;35:*.dl=01;35:*.xcf=01;35:*.xwd=01;35:*.yuv=01;35:*.cgm=01;35:*.emf=01;35:*.ogv=01;35:*.ogx=01;35:*.aac=00;36:*.au=00;36:*.flac=00;36:*.m4a=00;36:*.mid=00;36:*.midi=00;36:*.mka=00;36:*.mp3=00;36:*.mpc=00;36:*.ogg=00;36:*.ra=00;36:*.wav=00;36:*.oga=00;36:*.opus=00;36:*.spx=00;36:*.xspf=00;36:', 'PROMPT_COMMAND': 'history -a', 'REACT_EDITOR': 'none', 'SSH_CONNECTION': '127.0.0.1 37258 127.0.0.1 22', 'ANDROID_HOME': '/home/pdxjohnny/Android/Sdk', 'TERM': 'screen-256color', 'USER': 'pdxjohnny', 'TMUX_PANE': '%2', 'PYTHON': 'python3', 'WWW_HOME': 'https://google.com', 'SHLVL': '2', 'PS1': '$ ', 'SSH_CLIENT': '127.0.0.1 37258 22', 'LC_ALL': 'en_US.UTF8', 'GCC_COLORS': 'error=01;31:warning=01;35:note=01;36:caret=01;32:locus=01:quote=01', 'BROWSER': 'none', 'PATH': '/home/pdxjohnny/bin:/home/pdxjohnny/.yarn/bin:/home/pdxjohnny/.local/bin:/home/pdxjohnny/.yarn/bin:/home/pdxjohnny/.local/bin:/home/pdxjohnny/.yarn/bin:/home/pdxjohnny/.local/bin:/home/pdxjohnny/.yarn/bin:/home/pdxjohnny/.local/bin:/usr/local/bin:/usr/bin:/bin:/usr/games:/home/pdxjohnny/.bin:/home/pdxjohnny/.bin', 'HISTFILESIZE': '20000000', 'PDXJOHNNY_BIN': '/home/pdxjohnny/.bin', 'SSH_TTY': '/dev/pts/1', 'GOPATH': '/home/pdxjohnny/Documents/go', '_': '/home/pdxjohnny/.local/bin/alice'})}
DEBUG:dffml.CloneGitRepoImplementationContext:['git', 'clone', 'https://github.com/pdxjohnny/testa', '/tmp/dffml-feature-git-dv6e09vs']: Subprocess.STDERR_READLINE: Cloning into '/tmp/dffml-feature-git-dv6e09vs'...
DEBUG:dffml.CloneGitRepoImplementationContext:['git', 'clone', 'https://github.com/pdxjohnny/testa', '/tmp/dffml-feature-git-dv6e09vs']: Subprocess.STDERR_READLINE: warning: You appear to have cloned an empty repository.
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: {'repo': {'URL': 'https://github.com/pdxjohnny/testa', 'directory': '/tmp/dffml-feature-git-dv6e09vs'}}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] git_repo_default_branch
DEBUG:dffml.MemoryOrchestratorContext:[https://github.com/pdxjohnny/testa]: dispatch operation: git_repo_default_branch
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:git_repo_to_alice_git_repo
DEBUG:dffml.MemoryOrchestratorContext:[https://github.com/pdxjohnny/testa]: dispatch operation: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:git_repo_to_alice_git_repo
DEBUG:dffml.MemoryLockNetworkContext:Operation(name='git_repo_default_branch', inputs={'repo': git_repository}, outputs={'branch': git_branch}, stage=<Stage.PROCESSING: 'processing'>, conditions=[no_git_branch_given], expand=[], instance_name='git_repo_default_branch', validator=False, retry=0) acquiring: e8e321ea-2011-46cc-96d7-657884b14783(GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa'))
DEBUG:dffml.MemoryLockNetworkContext:Acquiring: e8e321ea-2011-46cc-96d7-657884b14783(GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa')) (now held by Operation(name='git_repo_default_branch', inputs={'repo': git_repository}, outputs={'branch': git_branch}, stage=<Stage.PROCESSING: 'processing'>, conditions=[no_git_branch_given], expand=[], instance_name='git_repo_default_branch', validator=False, retry=0))
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: git_repo_default_branch
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'repo': GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa')}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {'no_git_branch_given': True}
DEBUG:dffml_feature_git.util:proc.create: ('git', 'branch', '-r')
DEBUG:dffml.MemoryLockNetworkContext:Operation(name='alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:git_repo_to_alice_git_repo', inputs={'repo': git_repository}, outputs={'result': AliceGitRepo}, stage=<Stage.PROCESSING: 'processing'>, conditions=[], expand=[], instance_name='alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:git_repo_to_alice_git_repo', validator=False, retry=0) acquiring: e8e321ea-2011-46cc-96d7-657884b14783(GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa'))
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: None
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryLockNetworkContext:Acquiring: e8e321ea-2011-46cc-96d7-657884b14783(GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa')) (now held by Operation(name='alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:git_repo_to_alice_git_repo', inputs={'repo': git_repository}, outputs={'result': AliceGitRepo}, stage=<Stage.PROCESSING: 'processing'>, conditions=[], expand=[], instance_name='alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:git_repo_to_alice_git_repo', validator=False, retry=0))
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayOperationsGit:git_repo_to_alice_git_repo
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'repo': GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa')}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: {'result': GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa')}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] alice.cli.AlicePleaseContributeRecommendedCommunityStandards:has_readme
DEBUG:dffml.MemoryOrchestratorContext:[https://github.com/pdxjohnny/testa]: dispatch operation: alice.cli.AlicePleaseContributeRecommendedCommunityStandards:has_readme
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:meta_issue_body
DEBUG:dffml.MemoryOrchestratorContext:[https://github.com/pdxjohnny/testa]: dispatch operation: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:meta_issue_body
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:readme_issue
DEBUG:dffml.MemoryOrchestratorContext:[https://github.com/pdxjohnny/testa]: dispatch operation: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:readme_issue
DEBUG:dffml.MemoryLockNetworkContext:Operation(name='alice.cli.AlicePleaseContributeRecommendedCommunityStandards:has_readme', inputs={'repo': AliceGitRepo}, outputs={'result': repo.directory.readme.exists}, stage=<Stage.PROCESSING: 'processing'>, conditions=[], expand=[], instance_name='alice.cli.AlicePleaseContributeRecommendedCommunityStandards:has_readme', validator=False, retry=0) acquiring: e8e321ea-2011-46cc-96d7-657884b14783(GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa'))
DEBUG:dffml.MemoryLockNetworkContext:Acquiring: e8e321ea-2011-46cc-96d7-657884b14783(GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa')) (now held by Operation(name='alice.cli.AlicePleaseContributeRecommendedCommunityStandards:has_readme', inputs={'repo': AliceGitRepo}, outputs={'result': repo.directory.readme.exists}, stage=<Stage.PROCESSING: 'processing'>, conditions=[], expand=[], instance_name='alice.cli.AlicePleaseContributeRecommendedCommunityStandards:has_readme', validator=False, retry=0))
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: alice.cli.AlicePleaseContributeRecommendedCommunityStandards:has_readme
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'repo': GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa')}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: {'result': False}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryLockNetworkContext:Operation(name='alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:meta_issue_body', inputs={'repo': AliceGitRepo, 'readme_issue': ReadmeIssue, 'readme_path': ReadmePath}, outputs={'result': MetaIssueBody}, stage=<Stage.PROCESSING: 'processing'>, conditions=[], expand=[], instance_name='alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:meta_issue_body', validator=False, retry=0) acquiring: e8e321ea-2011-46cc-96d7-657884b14783(GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa'))
DEBUG:dffml.MemoryLockNetworkContext:Acquiring: e8e321ea-2011-46cc-96d7-657884b14783(GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa')) (now held by Operation(name='alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:meta_issue_body', inputs={'repo': AliceGitRepo, 'readme_issue': ReadmeIssue, 'readme_path': ReadmePath}, outputs={'result': MetaIssueBody}, stage=<Stage.PROCESSING: 'processing'>, conditions=[], expand=[], instance_name='alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:meta_issue_body', validator=False, retry=0))
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:meta_issue_body
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'repo': GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa'), 'readme_issue': None, 'readme_path': None}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: {'result': '- [ ] None'}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryLockNetworkContext:Operation(name='alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:readme_issue', inputs={'repo': AliceGitRepo, 'title': ReadmeIssueTitle, 'body': ReadmeIssueBody}, outputs={'result': ReadmeIssue}, stage=<Stage.PROCESSING: 'processing'>, conditions=[], expand=[], instance_name='alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:readme_issue', validator=False, retry=0) acquiring: e8e321ea-2011-46cc-96d7-657884b14783(GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa'))
DEBUG:dffml.MemoryLockNetworkContext:Acquiring: e8e321ea-2011-46cc-96d7-657884b14783(GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa')) (now held by Operation(name='alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:readme_issue', inputs={'repo': AliceGitRepo, 'title': ReadmeIssueTitle, 'body': ReadmeIssueBody}, outputs={'result': ReadmeIssue}, stage=<Stage.PROCESSING: 'processing'>, conditions=[], expand=[], instance_name='alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:readme_issue', validator=False, retry=0))
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:readme_issue
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'repo': GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa'), 'title': 'Recommended Community Standard: README', 'body': 'References:\n- https://docs.github.com/articles/about-readmes/'}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygithubissue:ReadmeIssueImplementationContext:Running ['gh', 'issue', 'create', '-R', 'https://github.com/pdxjohnny/testa', '--title', 'Recommended Community Standard: README', '--body', 'References:\n- https://docs.github.com/articles/about-readmes/'], {}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] alice.cli.AlicePleaseContributeRecommendedCommunityStandards:create_readme_file
DEBUG:dffml.MemoryOrchestratorContext:[https://github.com/pdxjohnny/testa]: dispatch operation: alice.cli.AlicePleaseContributeRecommendedCommunityStandards:create_readme_file
DEBUG:dffml.MemoryOperationImplementationNetworkContext:[DISPATCH] alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:create_meta_issue
DEBUG:dffml.MemoryOrchestratorContext:[https://github.com/pdxjohnny/testa]: dispatch operation: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:create_meta_issue
DEBUG:dffml.MemoryLockNetworkContext:Operation(name='alice.cli.AlicePleaseContributeRecommendedCommunityStandards:create_readme_file', inputs={'repo': AliceGitRepo, 'has_readme': repo.directory.readme.exists, 'readme_contents': repo.directory.readme.contents}, outputs={}, stage=<Stage.PROCESSING: 'processing'>, conditions=[], expand=[], instance_name='alice.cli.AlicePleaseContributeRecommendedCommunityStandards:create_readme_file', validator=False, retry=0) acquiring: e8e321ea-2011-46cc-96d7-657884b14783(GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa'))
DEBUG:dffml.MemoryLockNetworkContext:Operation(name='alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:create_meta_issue', inputs={'repo': AliceGitRepo, 'body': MetaIssueBody, 'title': MetaIssueTitle}, outputs={'result': MetaIssue}, stage=<Stage.PROCESSING: 'processing'>, conditions=[], expand=[], instance_name='alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:create_meta_issue', validator=False, retry=0) acquiring: e8e321ea-2011-46cc-96d7-657884b14783(GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa'))
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygithubissue:ReadmeIssueImplementationContext:['gh', 'issue', 'create', '-R', 'https://github.com/pdxjohnny/testa', '--title', 'Recommended Community Standard: README', '--body', 'References:\n- https://docs.github.com/articles/about-readmes/']: Subprocess.STDOUT_READLINE: https://github.com/pdxjohnny/testa/issues/15
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: None
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryLockNetworkContext:Acquiring: e8e321ea-2011-46cc-96d7-657884b14783(GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa')) (now held by Operation(name='alice.cli.AlicePleaseContributeRecommendedCommunityStandards:create_readme_file', inputs={'repo': AliceGitRepo, 'has_readme': repo.directory.readme.exists, 'readme_contents': repo.directory.readme.contents}, outputs={}, stage=<Stage.PROCESSING: 'processing'>, conditions=[], expand=[], instance_name='alice.cli.AlicePleaseContributeRecommendedCommunityStandards:create_readme_file', validator=False, retry=0))
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: alice.cli.AlicePleaseContributeRecommendedCommunityStandards:create_readme_file
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'repo': GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa'), 'has_readme': False, 'readme_contents': "# My Awesome Project's README"}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: None
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryLockNetworkContext:Acquiring: e8e321ea-2011-46cc-96d7-657884b14783(GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa')) (now held by Operation(name='alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:create_meta_issue', inputs={'repo': AliceGitRepo, 'body': MetaIssueBody, 'title': MetaIssueTitle}, outputs={'result': MetaIssue}, stage=<Stage.PROCESSING: 'processing'>, conditions=[], expand=[], instance_name='alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:create_meta_issue', validator=False, retry=0))
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Stage: PROCESSING: alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:create_meta_issue
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Inputs: {'repo': GitRepoSpec(directory='/tmp/dffml-feature-git-dv6e09vs', URL='https://github.com/pdxjohnny/testa'), 'body': '- [ ] None', 'title': 'Recommended Community Standards'}
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Conditions: {}
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygithubissue:CreateMetaIssueImplementationContext:Running ['gh', 'issue', 'create', '-R', 'https://github.com/pdxjohnny/testa', '--title', 'Recommended Community Standards', '--body', '- [ ] None'], {}
DEBUG:dffml.AliceCliAlicepleasecontributerecommendedcommunitystandardsoverlaygithubissue:CreateMetaIssueImplementationContext:['gh', 'issue', 'create', '-R', 'https://github.com/pdxjohnny/testa', '--title', 'Recommended Community Standards', '--body', '- [ ] None']: Subprocess.STDOUT_READLINE: https://github.com/pdxjohnny/testa/issues/16
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: None
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryInputNetworkContext:Received https://github.com/pdxjohnny/testa result {} from <dffml.df.memory.MemoryOrchestratorContext object at 0x7fc8080c1ca0>
DEBUG:dffml.MemoryInputNetworkContext:Received https://github.com/pdxjohnny/testa result {} from <dffml.df.memory.MemoryOrchestratorContext object at 0x7fc8080c1ca0>
DEBUG:dffml.MemoryOperationImplementationNetworkContext:Outputs: None
DEBUG:dffml.MemoryOperationImplementationNetworkContext:---
DEBUG:dffml.MemoryOrchestratorContext:ctx.outstanding: 1
DEBUG:dffml.MemoryInputNetworkContext:Received 05314e90cdad733c273ed02327a97702f2b4f441db1ee26a5a27b141e22f3c80 result {} from <dffml.df.memory.MemoryOrchestratorContext object at 0x7fc808105070>
DEBUG:dffml.MemoryOrchestratorContext:ctx.outstanding: 0
https://github.com/pdxjohnny/testa {}
05314e90cdad733c273ed02327a97702f2b4f441db1ee26a5a27b141e22f3c80 {} |
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-
graph TD
subgraph a759a07029077edc5c37fea0326fa281[Processing Stage]
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35b46df24b1604a456bd487c41525426 --> 39604f2a099b470fa14e075d9fe480bb
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39604f2a099b470fa14e075d9fe480bb --> 24a1136d076c239f151da2f4ca3c7825
end
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735041fe58840530e39ede1e7ecd06e0[alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:meta_issue_body]
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2965094fb920d11e73dd622c881e2d09(result)
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4f647ec6a92d622243eac92fcad91c17[alice.cli.AlicePleaseContributeRecommendedCommunityStandardsOverlayGitHubIssue:readme_commit_message]
7eab9107cbb22d2eadc5962b8ecb74c7(issue_url)
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subgraph inputs[Inputs]
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128516cfa09b0383023eab52ee24878a(seed<br>dffml.util.cli.CMD)
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end
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graph TD
subgraph a759a07029077edc5c37fea0326fa281[Processing Stage]
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Starting a new project with Alice$ gh repo create -y --private $USER/test-0001$ alice please contribute -log debug -repos https://github.com/$USER/test-0001 -- recommended community standards
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Volume 1: Chapter 4: Traveler of the EdgeAlice will use chadig.com and nahdig.com plus DIDs to deliver manifest schema in terms of allowlist and blocklist. She will also use these domains for running workloads. Trusted on chadig.com, untrusted on nahdig.com. She will run workloads in the cloud and on-prem (edge) servers. She will provision infra then run k8s jobs. She’ll run the previous jobs en mass given a source of repo urls. |
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Volume 0: Chapter 1: Peace at LastClear your mind. Focus. Imagine a message from Alice, on a rock, on the sea shore. On We pick up a the shell of a sand dollar once filled with angels. Meditate for a moment, look at the waves crashing in front of you We sit with her quietly for a moment. We know that Alice is Alice is older now, wiser, she's coming back to Wonderland and Our focus will be on crystal clear communication of thought. Remember Alice's message. References: |
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Volume 0: Chapter 2: She's arriving when?Between the tick and the tock. Which is not a finite amount ....
In the future we will travel to the past, to the days of References: |
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Volume 0: Chapter 3: A Shell for a GhostPlan for this tutorial:
Alice is the ghost in the shell. We know she's in there, References: |
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Volume 1: Chapter 3: Our Strategic Principles Guide Our Game PlanWe'd like to be able to ask Alice for a rolled up view of how our org At time of writing we do not yet have dependency tree creation flushed $ alice please contribute report on innersource healthThe following is an example report InnerSource Org HealthOverall maps to our general Good/Bad for a train of thought.
pie title Overall
"Minimum health or above" : 100
"Less than minimum health" : 100
Show dataflow of only connections to the overall calculation. graph LR
overall[Overall]
has_readme[Has Readme]
has_code_of_conduct[Has Code of Conduct]
has_security[Has Security]
has_contributing[Has Contributing]
has_license[Has License]
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Links to Repo Metric Visualizations
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Discussion thread locked and discussion should continue in 2nd Party PRDocs here: https://github.com/intel/dffml/tree/alice/docs/tutorials/rolling_alice |
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Rolling Alice: Volume 0: Architecting Alice: Preface
Rolling Alice
In this 7 volume tutorial series we roll Alice. This series will be written a chapter per quarter, over the next 1-2 years. Open Architecture Working Group meeting to parallelize workstreams mid June comment here to request invite. Link will be posted sometime in June here as well.
Alice’s architecture, the open architecture, is based around thought. She communicates thoughts to us in whatever level of detail or viewed through whatever lens one wishes. She explores trains of thought and responds based on triggers and deadlines. She thinks in graphs, aka trains of thought, aka chains of system contexts. She operates in parallel, allowing her to represent N different entities.
Table Of Contents
Volume 0: Architecting Alice
Volume 1: Coach Alice
Volume 2: Alice and the Art of Strategy
Volume 3: Alice and the Strategy of Art
Volume 4: Alice and the Health of the Ecosystem
Volume 5: Alice's Adventures In Wonderland
shaXXXsumbut they do know how to pipe to our listening TCP socket withcat < build/binary > /dev/tcp/192.168.1.20/7777we should just accept that as fine, why? Because we understand that the context of the situation is such that we aren't going to get provenance (okay you who's going to go put your grubby hands on your friends laptop to run sha sum stop that, they don't want you to touch their keyboard with your Cheeto hands. Hashing is not a mitigation that is not available to you! It has been declared as an unmitigated risk within the threat model, and we are running it anyway! because we checked the context within which this was being deployed and said the risk is acceptable.)Volume 6: Alice are you Rolling?
Creativity
Volume 7: Through The Looking Glass
Priority Number 1
Provide a clear, meticulously validated, ubiquitously adopted reference architecture for a freedom, privacy, security, and happiness preserving egalitarian Artificial General Intelligence (AGI).
To do so we must enable the AGI with the ability to act in response to the current system context where it understands how to predict possible future system contexts and understands which future system contexts it wishes to pursue are acceptable according to guiding strategic plans (such as do no harm). We must also ensure that human and machine can interact via a shared language, the open architecture.
Background
AI has the potential to do many great things. However, it also has the potential to to terrible things too. Recently there was an example of scientists who used a model that was good a generating life saving drugs, in reverse, to generate deadly poisons. GPU manufacturers recently implemented anti-crypto mining features. Since the ubiquitous unit of parallel compute is a GPU, this stops people from buying up GPUs for what we as a community at large have deemed undesirable behavior (hogging all the GPUs). There is nothing stopping those people from buying for building their own ASICs to mine crypto. However, the market for that is a subset of the larger GPU market. Cost per unit goes up, multi-use capabilities go down. GPU manufacturers are effectively able to ensure that the greater good is looked after because GPUs are the ubiquitous facilitator of parallel compute. If we prove out an architecture for an AGI that is robust, easy to adopt, and integrates with the existing open source ecosystem, we can bake in this looking after the greater good.
Security Considerations
As we democratize AI, we must be careful not to democratize AI that will do harm. We must think secure by default in terms of architecture which has facilities for guard rails, baking safety into AI.
Failure to achieve ubiquitous adoption of an open architecture with meticulously audited safety controls would be bad. The best defense is a good offense.
Notes
Much of this discussions thread are notes and scratch work around the purpose and future of the project. Everything here will be converted to ADRs, issues, code, etc. as appropriate. We as a community (open to everyone) will work together to map our our activities to achieve these goals. We will document our process along the way and write these series of tutorials to show others how they can understand and extend the open architecture (Alice).
This thread is a central place for everyone interested to participate and collaborate. There are many pieces to this plan that need to be driven by many individuals to make this all happen. Reach out or just start commenting if you want to get involved.
References
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