I have been working on my research/developer portfolio for some time - and have gomr through many iterations. I think I am now somewhere I think I like things, but I get more and more distaste towards the formatting, the longer I look at it.
Here are some mobile snaps of the layout, but I could use a bit of feedback.
Just a growing thought experiment I have been chewing on for a bit. I initially shared as a comment to a user who was a bit defeated, in regards to AI's current place in our world , and it's effect on humanity as a whole. While, yes, there will be perhaps more bad than good that comes from its widespread adoption - however it is here to stay, and it should be put to good use if it is going to exist and grow.
A lot of applications I see AI being used for seem to paint it's potential in a bad light. However, there are nunerous applications where it can be harnessed and applied to real world problems, and even find solutions where people could not previously. It just depends on how rigorous your standards are as a researcher, and by holding the model and the work to a high standard. I am successfully in the process of building a completely agent/model driven research laboratory, and aim to apply it directly into a scaling robotics and bio-engineering firm, shifting into difficult fields where the accountable engine I built would accelerate discoveries and advancements.
I guess having the right application of mind, and an appropriate curiosity - but a lot truly can be done with AI. A lot like what this dude was experiencing earlier in his career, is still occuring today.
https://youtu.be/Oojrfdl42LI?is=c-svq2kC5lJs-C\\\\\\\_Q
However, if applied for the right purposes AI can be of use to society and humanity. It may even help solve some of our deepest, longest standing problems. I can't say it will save or destroy the world, but as a researcher and an academic, it can be used to learn like no other learning tool could before. Just be careful about sycophancy, and criticize your own ideas somewhat before just diving in headfirst - or letting the model take you for a ride.
https://harperz9.github.io/research-c3-thermodynamic.html
https://harperz9.github.io/research-discovery-forge.html
https://harperz9.github.io/research-learning-forge.html
https://harperz9.github.io/research-formal-replay-preflight.html
https://harperz9.github.io/demo-emet.html
https://harperz9.github.io/demo-index.html
https://harperz9.github.io/research-conferred-existence.html
https://harperz9.github.io/research-witness-and-verification.html
https://harperz9.github.io/research-conservation-of-faithfulness.html
A lot of applications I see AI being used for seem to paint it's potential in a bad light. However, there are nunerous applications where it can be harnessed and applied to real world problems, and even find solutions where people could not previously. It just depends on how rigorous your standards are as a researcher, and by holding the model and the work to a high standard. I am successfully in the process of building a completely agent/model driven research laboratory, and aim to apply it directly into a scaling robotics and bio-engineering firm, shifting into difficult fields where the accountable engine I built would accelerate discoveries and advancements.
I guess having the right application of mind, and an appropriate curiosity - but a lot truly can be done with AI. A lot like what this dude was experiencing earlier in his career, is still occuring today.
https://youtu.be/Oojrfdl42LI?is=c-svq2kC5lJs-C\\\_Q
However, if applied for the right purposes AI can be of use to society and humanity. It may even help solve some of our deepest, longest standing problems. I can't say it will save or destroy the world, but as a researcher and an academic, it can be used to learn like no other learning tool could before. Just be careful about sycophancy, and criticize your own ideas somewhat before just diving in headfirst - or letting the model take you for a ride.
https://harperz9.github.io/research-c3-thermodynamic.html
https://harperz9.github.io/research-discovery-forge.html
https://harperz9.github.io/research-learning-forge.html
https://harperz9.github.io/research-formal-replay-preflight.html
https://harperz9.github.io/demo-emet.html
https://harperz9.github.io/demo-index.html
https://harperz9.github.io/research-conferred-existence.html
https://harperz9.github.io/research-witness-and-verification.html
https://harperz9.github.io/research-conservation-of-faithfulness.html
I am building Project Telos, a public repo-backed workbench for making AI-assisted work more observable and testable. One of the five flagship environments is an artist/studio lane: RAW image work, gallery output, and the surrounding receipt trail that lets someone inspect what happened instead of only seeing a final image.
The part I would like graphics-programming eyes on is the implementation shape rather than the launch itself:
- What metadata should a RAW-to-gallery workflow preserve so later reviewers can evaluate color/tonal decisions without guessing?
- What should be visible in a receipt trail for transforms, preview generation, exports, and human edits?
- Where would you draw the line between useful automation and hiding the actual image-processing decisions?
- If this becomes a benchmark/test environment, what would you want measured first: reproducibility, color management, compression/export artifacts, speed, or reviewer ergonomics?
Public field guide:
https://harperz9.github.io/field-guide.html
Main site:
Repos/profile:
https://github.com/HarperZ9/gather
https://github.com/HarperZ9/index
https://github.com/HarperZ9/forum
https://github.com/HarperZ9/crucible
https://github.com/HarperZ9/telos
Stage label: solo, pre-revenue, author-tested, not independently audited. crucible is developing in parallel; this post is about getting public review/testing on the graphics-facing workflow, not about that package.
I am looking for verification, testing, early traction, and pointers to people who would stress-test the workflow properly. If anyone knows modest grassroots research-funding paths for open tooling like this, pointers are welcome, but technical critique is the main ask.
Past a handful of repositories, the shape of a codebase usually lives in someone's head. I built index to draw that shape from evidence instead.
It maps a workspace of git repos, records the file and line behind every dependency edge, assigns structural roles, and emits a certificate you can rerun instead of trusting. The core design constraint is deliberately boring: pure Python standard library, no API, no account, no network, deterministic output.
The workflow I care about most:
The tool is strongest for Python internals because it reads the AST. Other ecosystems are best-effort and bounded in the protocol docs rather than hidden behind a false certainty claim. It is meant to remove toil from codebase orientation and give agents or humans a stable structural map before they make changes.
Install: pip install index-graph Repo: https://github.com/HarperZ9/index Main site: https://harperz9.github.io GitHub: https://github.com/HarperZ9
The broader Telos line this sits inside:
Looking for verification/testing on real multi-repo workspaces, technical pushback on the certificate model, early traction from builders who actually rerun it, and possibly grassroots research funding for the larger checkable-state line.
The failure mode I keep running into is not "the model is dumb." It is that the model and the person are not looking at the same witnessed state. The model says it checked a file, a page, a claim, or a tool result, and all I have is a fluent report.
The primitive I keep rebuilding is: perceive the artifact in a way that can be re-derived, check it against a criterion the model did not author, and return MATCH / DRIFT / UNVERIFIABLE. Not "trusted."
I have been building a five-flagship line around that: gather for witnessed intake, index for rerunnable workspace maps, forum for accountable multi-agent orchestration, crucible for measured judgment and refinement, and the telos engine for shared perceive-and-make work. crucible is now public on GitHub as a 0.4.0 release candidate: the judgment organ exists, the refine loop is complete, and PyPI/package publication is still pending.
Question for this sub: where does this break as systems get more capable? Is the bottleneck raw intelligence, or the lack of shared, checkable state?
the demonstration is more of a creative showcase, to allow people a chance to see what the potential here is - the model is a low memory local model uploaded to a backend droplet server, so scaling and infrastructure will be necessary - but I think I may have engineered the start of something that may change computing forever - I could be wrong, but this is an ecosystem of projects I have been working on in private for ~2 years, and I hope this provides a simple enough showcase of what the possibilities are with a bit more funding or adoption publically. Everything is open source, but I am still trying to ship the new visual engine to my website - Check it out! ps sorry bout the bad(no) edit and recording - it is just a lil mockup - Until I ship the renderer update to the engine, here is a brief description of the project - https://harperz9.github.io/overview.html -
I think that the next change I need to make is that when screenshare is on - the sensorimeters, and the context window compress into one GUI, and it will be less cluttered. Not sure, but let me know if I am drinking the kool-aid - or if I am onto something. I also will try to capture a better recording that will allow a better demonstration of my interactions with live subject matter like television or youtube
Basically it is a two-way embedded system that transpiles data into a streamed, perceptible form - and then renders that data in a way that you can see, and in a way the model can see with you. There are measuremeters and actuatators built in, so soon the model will be able to perform agent assisted work, and you will be able to actually watch it work
MCP server, perceive→gate→act→verify→witness. Gate = default-deny, config the agent can't modify from inside; no permit → no execution. Witness = re-derivable perceptions (not a screenshot), journaled actions. Verdicts MATCH/DRIFT/UNVERIFIABLE, never silent. Siblings coherence-membrane (868), EMET (3 impls). Honest stage: solo, pre-revenue, 201 tests — kick the gate logic, I want to know where it leaks. github.com/HarperZ9/accountable-surface.
The effect/lifetime check is a hard gate before lowering, not advisory; can't prove sound → codegen never runs. C backend (the tested path), current cargo test 1002 passed / 0 failed / 11 ignored.
Questions: where's the line between "checker rejects" and "backend asserts/refuses"? Strategies to keep emitted C from UB-ing past your guarantees (aliasing, returned-pointer lifetimes)? Solo/author-tests — want adversarial cases that should be rejected but slip to codegen.
Honest caveat: "fails closed" = design intent enforced by author tests, not a machine-checked proof.
Built an MCP server for bounded, auditable agent autonomy. It exposes `perceive` / `propose` / `interocept` / `session_journal`:
Wire-in is a normal `mcpServers` block (env points at your grants file + journal). 129 tests; zero external deps in the core, the MCP server is the only edge-adapter. Honest limit: the gate is advisory until a runtime enforces it.
Repo + the MCP config: github.com/HarperZ9/accountable-surface. Feedback and teardowns welcome.
It is now, on a day to day basis - transitioning from theory to reality, and I am needing second opinions. I am working on a large directory of tools that I think may be one of the keys to unlocking both more responsible AI, and more capable AI. And I am eager to hear some feedback on the thesis, and provide tests on the tooling. This was flaired philosophy, as that is where the project direction was founded from - I want to see if this is grounded, and the best way to do it is through third-party verification.
Reading on the subject matter available here at github. https://harperz9.github.io
It is now, on a day to day basis - transitioning from theory to reality, and I am needing second opinions. I am working on a large directory of tools that I think may be one of the keys to unlocking both more responsible AI, and more capable AI. And I am eager to hear some feedback on the thesis, and provide tests on the tooling. This was flaired philosophy, as that is where the project direction was founded from - I want to see if this is grounded, and the best way to do it is through third-party verification.
Reading on the subject matter available here at github. https://harperz9.github.io
Been dedicating years of my life in private to learning systems engineering, and systems architecture as a hobby in my free time. Probably somewhere around ~60,000 hours of independent work, mostly out of enjoyment and curiosity, rather than aiming to build toward employment prospects. I would not consider myself exceptionally skilled or knowledgeable, and certainly not well credentialed.
However, what I do have is stubbornness. I dedicate time to problems until a solution can be found, and am willing to set other things aside to focus on creating or finding that solution. It is one of the only things that seems to bring me enjoyment.
Lots of stuff I think I have constructed is useful and serves a purpose, but nobody to share it with, and not a lot of good uses for it on an personal scale. That coupled with having nowhere to express those skills, where it pays off. At least in a sustenance or security sense.
This was something I was mostly alright with, and willing to accept - up until the last 2 weeks or so, when I was laid off from my previous career due to a business transition, and restructuring. Prior to that if something I made wasn't useful to me, I could just build something else that brings me enjoyment on the next side project. Things were pretty alright, if you ask me.
I was left in the dark on this business decision, and when the notice was given I was unprepared. There was not anything to indicate this was coming.
I have been in the same industry, and at the same employer for 11 years in a completely unrelated field. And I am now left scrambling to find employment so I can continue to provide a home for my family in the coming months. I am willing to take on just about anything, and can feel my way around most any architecture or system. Open to freelance contracts, or employment positions.
I will link my resume. There will be a contact email listed - as well as my portfolio. Thank you, Zain. :)
Hello, my name is Zain. :)
I am hoping to reach out in an attempt at making some money to support myself while I am making a complete transition to this sector from a citizen science field in my previous full-time gig. I am preparing to have a son, and the ohysicality and inconsistency has led me to start looking at better prospects elsewhere. I know the market is rough right now, but I also believe a lot of the specializations I spend the most time in on a day to day basis has a lot of relevancy and value in today's technological frontiers on most surfaces.
I have been quietly working on a massive backlog of AI accountability and safety infrastructure, and I would love to begin integrating the tooling into consulting work, as well as allow it to remain a valuable tool I can carry with me as an employee or contractor. I also have experience in cybersecurity, red team work, and pentesting engagements - as well as experience with reverse engineering.
I don't have a lot of contacts in this sector, so I am coming to a point where reaching out and just plainly showing what I am working on and what my skills are is a better showcase than anything I could tell you. So here is my portfolio, and if you are interested you may contact me through the email on my portfolio page.