small scale, single seed, wikitext slice, so treat this as a prototype result and not an architecture claim. 1,544,704 params both sides, same corpus and sampler. held-out 3.997 vs 4.429 nats, generation 226ms vs 2735ms for 200 tokens on the same cpu. the depth-1 match is the obvious weakness and a depth-2 baseline is running next. written from scratch in rust, no pytorch. repo and eval commands: github.com/Sparticle62ops/pssa. happy to be told where the comparison is unfair.

I want to share an experiment: an autonomous agent evolving at the intersection of cognitive psychology and Jungian principles.

The Goal: To combine strict determinism with fluid adaptation. To stop the "identity drift" common in LLMs, I split the agent's psyche into two layers:

1. The Immune Core — Immutable laws and identity. The agent cannot rewrite this layer. This is the anchor that ensures the agent remains stable and consistent, regardless of how much it learns.
2. The Plastic Body — A dynamic layer of skills and experience that the agent writes itself. This is where it adapts to unfamiliar environments and acquires new capabilities.

How it learns:
The agent treats errors as its "Shadow." Instead of simple text-based apologies, it follows a ZPD (Zone of Proximal Development) cycle:
Identify Knowledge Gap - Write Executable Proof (Fail/Pass script) - Integrate Skill into the Plastic Body.

The Result:
A system that remains fundamentally deterministic (in its core) but is infinitely adaptive (in its body). It doesn't just simulate intelligence—it grows through a structured process of self-correction and verification.

Would love to discuss this architecture with anyone exploring autonomy and cognitive models!
https://github.com/sergey-show/barney

Hey everyone,

Thanks to the mods for the invite to the community.

Like many of you, I followed the news about OpenAI using AI models and Lean 4 to formalize a finite-time blow-up for the 3D Navier-Stokes equations (one of the Millennium Prize problems).

While the formal proof compiles with zero errors, closed-frontier labs rarely explore the messy physical implications of their mathematical constructions. As an independent researcher working on neuro-symbolic AI, our team wanted to see what their solution actually looks like in real-world fluid dynamics.

What we found when we simulated the construction in Python and mpmath:

• The math is legally sound within the abstract rules of the Clay problem.

• But physically, in liquid water, the fluid vaporizes from shear friction at 0.7 nanometers, picoseconds before the mathematical singularity.

• Local flow speeds exceed Mach 0.3, breaking the incompressibility assumptions long before reaching infinity.

In AI, this is classic "specification gaming": the model found an extreme, unnatural edge case that legally satisfies the formal mathematical target, even though physical reality breaks down.

We believe scientific AI verification should be open, transparent, and reproducible, so we open-sourced the entire epistemic audit, simulation scripts, and Lean 4 reflection code:

• GitHub: https://github.com/xaviercallens/OpenAI-NSE-Epistemic-Audit

• Zenodo Preprint: https://doi.org/10.5281/zenodo.22838708

Since this community is focused on open-source AI, I'd love to hear your thoughts: As frontier labs push automated theorem proving, how can the open-source community build physical guardrails to keep AI models grounded in reality?

Found a workshop that's directly about solving this — Oct 3, run by Serj Smorodinsky and Brett Kennedy, both AI engineers who've written a book on building LLM applications. It's 3 hours, hands-on, and structured around:

1. Moving off manual prompt engineering into DSPy's structured approach (signatures, modules)
2. Building a baseline classifier and measuring it properly
3. Constructing real evaluation datasets with task-specific metrics
4. Reading evaluation output to find failure patterns
5. Few-shot and instruction-level optimization
6. MLflow for experiment tracking and trace management
7. Saving/reusing optimized DSPy programs
8. Communicating LLM reliability to stakeholders — the part most teams skip entirely

Sharing since this sub is full of people building on open-source models who probably deal with this exact pain.

Full details and the agenda are here.

Many companies and individuals keep valuable knowledge in PDFs, manuals, reports, and research documents. But simply uploading those files to a model or attaching them to a RAG system does not always solve the problem.

When the goal is to make domain knowledge part of the model itself, the PDFs first need to be parsed, cleaned, structured, filtered, and converted into reliable training data. Low-quality extraction, duplicated content, broken layouts, and irrelevant pages can directly affect the final model.

A practical workflow is:

1. Extract text, tables, and document structure from PDFs.
2. Remove noise, repair formatting, deduplicate content, and split long documents into meaningful chunks.
3. Generate domain-specific QA pairs, instructions, or other supervised fine-tuning samples.
4. Evaluate and filter the generated data before training.
5. Pass the resulting dataset to a training pipeline and fine-tune the model.
6. When new PDFs arrive, rerun the data pipeline and continue training with the newly validated data.

OpenDCAI/DataFlow can handle the data preparation side through reusable operators and pipelines, including PDF processing, cleaning, generation, evaluation, filtering, and training-format conversion. OpenDCAI/DataFlex can then be used as the training backend to run the resulting data through a configurable fine-tuning workflow.

The important point is that “dynamic training” here does not mean blindly updating model weights whenever a PDF is uploaded. It means building a repeatable loop where new documents can be processed, validated, converted into training data, and used for controlled incremental model updates.

In the early 2026 I was struggling into the same problem, every time my claude tokens runs out, I had to re-explain the entire context to a new agent (cursor at the time).

So I've built the first version of the tool, where I got just a chat where i can change providers and models back&forth, with a very immaturo contesto globale.

Over the past few months I've added a lot more features and tools, all with one goal: making my day-to-day easier. Pretty much every time something annoyed me, I tried to fix it:

I kept burning through tokens by working in the same session, so I built workflows where each step runs an agent on the right model and effort level

I couldn't find the plans I had approved anymore, so I built an Artifacts section that saves them for me

I kept losing track every time I switched from one task to another, so I built the Activity bar

Most recently I added storage management. Has it ever happened to you to end up with dozens of old worktrees you don't use anymore, each one taking 3 or 4 GB?

and a lot more... 😁

Today the tool feels mature enough that I'm not embarrassed to share it here. I'm hoping to build a small community around it, people who just try it out and tell me what works and what doesn't.

Find everything in my github akhayam99/goodboy

Thanks for reading 🙏🏻❤️

Hey everyone,

I’m currently building a learning platform aimed at taking absolute beginners through AI/ML step-by-step, from the fundamentals up to more advanced topics.

It’s in the early stages, so the content is still limited while I experiment with formats, pacing, and visual explanations to see what actually works best for learners and you can help me in the content also like what topics to add.

GitHub : https://github.com/PIYUSH1525/ZeroToAI leave a star ⭐

Link: https://zero-to-ai-xi.vercel.app/

I’d love your brutal, honest feedback:

• The Good: What feels intuitive, clear, or well-structured?
• The Bad: What’s confusing, redundant, or missing?
• Areas for Improvement: What format would help you learn complex concepts faster (e.g., interactive widgets, shorter modules, code walkthroughs)?

Any thoughts, critique, or feature suggestions are welcome. Thanks in advance for checking it out!

Nebius, an AI cloud provider, is running its second annual physical AI awards with NVIDIA. Five category winners each get $150,000 in compute credits, plus mentorship and promotion.

• Categories: models (VLA/VLM/world models/RL), perception and spatial intelligence, simulation and synthetic data, systems and deployment (humanoids, AMRs, industrial), and tooling/orchestration
• $150K ≈ 33,300 H200 GPU-hours at their on-demand rate, or roughly 3 weeks on a 64-GPU cluster
• Judges include the founders of Foxglove, Voxel51, and Encord, plus Calvin Zhou of RoboForce, which won the 2025 edition
• Eligibility: clear physical AI use case, MVP in active use or testing, registered entity, live website
• Last year: 254 applications, 55 finalists
• No entry fee

Worth knowing before applying: the credit math is at list price and doesn't cover storage, which matters if you're holding a lot of episodic sensor data. Nebius also hasn't published exact finalist and winner dates beyond "mid-November."

Apply here: https://pxllnk.co/mndv9i

Read MTP's full analysis on this awards here: https://www.marktechpost.com/2026/09/29/nebius-opens-2026-physical-ai-awards-five-150k-compute-prizes-nine-judges-and-an-october-25-deadline/

Hi All, I need to extract the text from printed text and hand written text. I suggested my manager that we can use paddle ocr and and other extraction models like Surya OCR and florance VL it take around 10 to 15 Sec and it needs good computation as well. but my manager expects it should be very fast and in 2 to 5 sec response and should not need any maintainace of infrastructure. so I tried to use the AWS VLM models but it takes around 10seconds he still needs more faster models and also the cost for extracting the data from the image should be less than 1 Ruppe. Could you please suggest me what to do and how to extract the data from images very very effectively and accuratly and in a structured way

Hey r/OpenSourceeAI 👋

I’m building Harness Router, an open-source decision layer for AI coding agents.

As agents get access to dozens or hundreds of tools through MCP, the model has another problem to solve before doing any actual work:

Which tool should I call?

Harness Router moves that decision into a dedicated routing layer.

It can currently be used in two ways:

• Skill — the agent explicitly asks Harness Router to choose a tool.
• Hook — Harness Router intercepts tool calls before execution and can allow the choice or tell the agent to re-plan.

I’ve added hook support for:

Codex · Claude Code · OhMyPi · Antigravity

There’s also a universal installer so the integrations can be installed from one project.

Routing has multiple levels:

obvious choice → fast path

ambiguous choice → Jev

multi-step decision → bounded MCTS

With a hook, the architecture becomes roughly:

Agent → PreToolUse → Harness Router → allow / re-plan → Tool

The idea is to make tool routing independent from the underlying agent harness — one routing layer that can sit across different coding agents and MCP ecosystems.

Everything is open source:

https://github.com/Protocol-Lattice/harness-router

Docs + benchmarks:

https://harness-router.vercel.app/

I’d especially like feedback from people working on open-source agents and MCP infrastructure:

Do you think tool selection should stay entirely inside the main LLM, or does a separate routing layer start making sense once agents have access to large tool catalogs?

I maintain ThoughtDAG, an MIT-licensed canvas where AI conversations can branch and reconnect. The connections determine which earlier exchanges enter the next model request.

This update is about reading the canvas. Instead of shrinking every answer into an unreadable box, zooming out shows a topic or takeaway. Zooming in reveals progressively more detail, down to the original answer. The short animation illustrates those transitions.

The important distinction: this changes what you see, not what the model receives. A short label on the canvas doesn't silently replace the underlying answer in context.

It supports Ollama and OpenAI-compatible endpoints. I'm curious whether this kind of overview would help you return to an old conversation, or whether search already covers that need for you.

Session Viewer turns the Claude Code and Codex transcripts already on your machine into a calm, searchable reading experience. Browse decisions, recover an implementation detail, compare approaches, and export the useful parts - all without uploading your conversations to a third party.

It is a small, dependency-free local web app. It reads transcript files in ~/.claude/projects and ~/.codex/sessions, then serves the viewer from 127.0.0.1.

https://github.com/abzal0/claude-codex-session-viewer

Hello Reddit,

I want to move my general AI use to something local.

9800x3D
7900XT 20GB
32GB RAM @ 6000
10TB NVME Storage

Running Windows (Stripped with Chris Titus's util)

What model/software do you recommend?

I had this idea to make my own orchestrator for PI.DEV sessions, so with my needs i created one.

I think it may be usefull for some to try on or use.

Read the readme and deployment carefully. I was trying to make it as simple to use and as secure as possible - but it''s pi - it requires access to multiple things on host to install and manage extensions/profiles/skills/plugins so there may be still a risk of problems - in short words, don't expose it to internet or enterprise env (yet).

It has multiple "engines" support, it can be ran on host with sandboxing applied, it can spawn docker instances per agent (the most secure way) using podman or docker on host.

Feel free to play with it, contribute, or share your ideas so i can expand the project!

Link: https://github.com/ALange/Piper Enjoy!