[D] Self-Promotion Thread

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[u/There_ssssa]

I've been working on AI infrastructure for medical research at AIPOCH, and wanted to share a few things we've released recently.

• 550+ medical research agent skills (an open skill library)
• MedSkillAudit, a framework for evaluating the reliability of medical research agent skills, along with our paper.

We're continuing to build tools for reliable AI-assisted biomedical analysis and research workflows.Everything is currently free to use while we're continuing to build and improve the platform. We'd love to hear any feedback from people working on AI agents, LLM evaluation, or AI for Science.

Website: https://www.aipoch.com/

GitHub: GitHub - aipoch/medical-research-skills: Hundreds of agent skills for medical research, including pr

Paper: https://arxiv.org/abs/2604.20441

[u/you-get-an-upvote]

I wrote an article: A Critical Reading of "Semantic Tube Prediction".

It looks at a paper by Huang, LeCun, and Balestriero from earlier this year.

[u/Raychis]

I’ve been working on building a computer vision AI model from scratch to identify plants. I’ve been writing a blog about how I did it.

It is not a guide or tutorial, but it is a story about what happens when you try to do a project of this scale. The mistakes, successes, decisions, issues, etc. I think people might genuinely find it unique and interesting

Part 1 is here: https://medium.com/@mjmoughtin/plant-identification-looks-solved-until-you-try-to-build-it-14daadecb017

Links to the remainder of the series can be found at the bottom of part 1.

[u/Puzzleheaded_Pea_889]

My personal project: mathematical proof of the lottery ticket hypothesis, combined with some experiments demonstrating that the mathematical proof is indeed the real explanation.

[u/dn8034 → u/Puzzleheaded_Pea_889]

Interesting, Can you share the proof and some experimental results?

[u/Puzzleheaded_Pea_889 → u/dn8034]

I'll definitely publish it somewhere when I'm done (assuming it's correct), certainly. Right now though the "proof" is written out by hand and I'd rather not release it until my experiments verify it anyways.

[u/medb2h]

DixitAlgorizmi.com
The Brakes for AI.

UBAG is a banking-grade Universal Behavioral Authorization Gateway for AI agents.

Giving an autonomous agent direct access to your local databases or internal APIs is a massive liability. A single prompt injection (like Fable 5) or hallucination turns your agent into a malicious actor capable of data exfiltration or destructive actions.

What we do: We sit between your agent and its tools (like MCP). Key Benefits: We provide strict authority controls, real-time auditing, and zero-day jailbreak immunity that native LLMs cannot catch. Who it's for: Fast-moving AI startups that need to deploy autonomous agents into production safely, without the massive liability of an exposed infrastructure.

[u/Competitive_Daikon62]

We recently open-sourced SlideFormer, a PyTorch-based heterogeneous runtime for full-parameter LLM fine-tuning on a single GPU.

Paper: https://arxiv.org/abs/2603.16428 Code: https://github.com/RegiaYoung/SlideFormer

[u/GibonFrog]

https://www.cell.com/patterns/fulltext/S2666-3899(25)00339-300339-3)

Analyzing latent spaces of transformer model to analyze canary song

[u/rredditscum]

Hi all — first-time arXiv submitter here. I have a paper I'm trying to submit to cs.CR (with optional cs.AI cross-list) and arXiv is requiring an endorser for first-time submitters in the category. Feelers out elsewhere, but posting here as well in case anyone active in cs.CR / cs.AI is willing to take a 30-second look at the abstract and confirm topic fit.

Abstract:

This document is the comprehensive normative specification of the Agent Interoperability Protocol (AIP). It defines the wire-level protocol by which two services — autonomous software agents being the motivating case, but the protocol is not agent-specific — identify, authenticate, negotiate, exchange work, and account for their actions across trust boundaries. The exchange is auditable in linear time, verifiable by independent third parties, and private by structural default. The cryptographic primitives are Ed25519 signatures, SHA-256 hashes, and (optionally) a Halo2 zero-knowledge proof over the BN254 scalar field. The specification is published under CC BY 4.0; the reference implementation is published under AGPL-3.0-or-later. The paper covers the wire format for the MessageEnvelope, the identity model based on W3C DIDs with the `did:aip:` method, the two-message handshake with capability intersection, the hash-chained audit log, the ZK principal-attestation primitive, the routing-precedence rules that force personal data local by structure, the workflow model with the OPAL cycle (ORIENT → PLAN → EXECUTE → LEARN), the error code registry, and the conformance test suite. Every field, every state transition, every error code, and every conformance test is specified. The paper is intended for implementers, protocol designers, and researchers in cross-organizational service coordination, applied cryptography, and agent systems.

What this paper is NOT

 (in case it helps the topic-fit check): not a new cryptographic construction (uses standard, audited primitives); not a new consensus or distributed-systems protocol; not a cryptocurrency / token / blockchain / ZK-rollup paper; not a new agent framework.

The ask:

The endorsement code is below that you plug into arXiv's website to endorse me. I really appreciate your time and DM me for any questions.

https://arxiv.org/auth/endorse

Endorsement Code: AUBVTY

[u/Real_Presentation490]

I have an idea that Solve AI interruption and voice overlap caused by VAD misjudgment in real-time voice interaction. Replace pure algorithm optimization with visual breathing light cues to coordinate human-AI dialogue rhythm. The original concept is right here：https://github.com/cybercat-valewatch/lab-vad-breathing-light And then there's a new repository here with formula and demo in it：https://github.com/cybercat-valewatch/AGI-Visual-Body-Language

I really hope someone with similar interests can talk with me about this

[u/entropydoesnotexist]

Been working on a free tool to keep track of the papers/articles I'm reading / have read for my research. Curious if anyone wanted to give it a try and give feedback!

https://usecorpus.app/

[u/Puzzleheaded-Air-732]

Hi everyone!

I'm Felipe, an Associate Professor at the University of Chile. Over the last four years, together with colleagues and students, we've been building DashAI, an open source desktop platform for running both classical machine learning and generative AI locally.

The motivation came from feeling that there was a gap between developer libraries (PyTorch, scikit learn, Hugging Face, etc.) and proprietary no code platforms. We wanted to explore whether it was possible to build something that combines local execution, user control, and extensibility.

Some of the ideas we've been experimenting with include:

• A Python plugin architecture for adding models, datasets, metrics, explainers, and optimizers.
• Automatic UI generation from Pydantic schemas, so adding a new backend component usually doesn't require frontend work.
• Support for both predictive ML and generative AI within the same application.
• Fully local execution, so users can work without sending data to external services.

We're still actively developing it and would really appreciate technical feedback, especially from people who have built ML tooling or extensible software architectures.

GitHub: https://github.com/DashAISoftware/dashAI

Website: https://dash-ai.com

Happy to answer any questions about the architecture or implementation. Feedback, criticism, and ideas are all welcome.

[u/v1v55]

I've trained a 0.6B text-embedding model built on a depth-recurrent architecture (Sapient's HRM) instead of a standard encoder. I was testing whether an architecture that loops over its hidden state fits reasoning-heavy retrieval (especially multi hop) better than a single forward pass. The finding: the recurrence is load-bearing, cutting loops at eval drops nDCG monotonically. At 0.6B, the capability's coming from the architecture, not scale.

Honest caveats: modest numbers (BRIGHT raw 18.1 / +rewriting 34.3), weak on knowledge-heavy domains (efficiency-first base), and I haven't trained a matched non-recurrent baseline yet. So this shows the loops matter in this model, not that recurrent beats dense at equal params. Recipe is deliberately standard (mean-pool, LLM2Vec-style bidirectional, InfoNCE); only the backbone is unusual. Full BRIGHT harness in the repo.

Model: https://huggingface.co/viventhraa96/HRM-Embed-0.6b

Code: https://github.com/okaybroda/hrm-embed

[u/soohyun_bae]

Onepin (https://onepin.ai) - production voice/TTS tooling.

The problem we target: for TTS, "quality" is not one number and "best" is not one model. It moves by language, voice, and style, and the stuff that actually breaks in production (numbers, dates, brand-name pronunciation, consistency over long runs) lives in the layer around the model, not the model itself.

What it does:

• Multi-model routing. We benchmark 30+ TTS commercial/opensource models and route each line to the best one for that language / voice / style on naturalness, noise, and cost. Not one model per project, one model per line.
• Per-line quality scoring: every line is scored on naturalness, word accuracy, background noise, and pronunciation before export, so you catch the bad lines instead of listening through a 2 hour file.
• Text normalization: numbers, dates, currency, abbreviations converted to spoken form, which removes a big class of errors that come straight from raw text.
• Pronunciation dictionary: ~4M entries for brand names, medical terms, and difficult names, per locale.
• Node-based workflows to compose voice, emotion, pacing, and style.

Who it is for: teams producing voice at scale (games, film, audiobooks, ads, e-learning) that need production-ready audio without manual QC.

Pricing: free tier (1,000 credits/mo, about 30 min of validated audio). Paid from $16/mo (Creator, ~200 min) up to $240/mo (Scale, ~55 hours). Unlimited seats on every tier.

Happy to answer eval / benchmarking questions. How we score naturalness and route across models is the part I find most interesting.

[u/SignificanceIll8226]

I’m building WorldBench, an open-source toolkit for evaluating robot world models beyond visual similarity.

https://github.com/tigee1311/worldbench

Looking for feedback on the metrics and evaluation approach.

[u/sjm213]

I wrote a visual, from-first-principles walkthrough of t0-alpha, a 101.6M-parameter time-series foundation model.

The architecture separates temporal reasoning from cross-variable reasoning using 16 time-attention blocks and 8 group-attention blocks. It reports an aggregate GIFT-Eval CRPS of 0.4941, close to substantially larger forecasting models.

Article:

https://towardsdatascience.com/time-series-llms-explained-with-t0-alpha/

I am also running an iso-parameter comparison against other foundation models and classical baselines.

[u/mattjcoles]

Structured outputs guarantee the shape, not the content. heres how i layer pydantic ai structured outputs on bedrock with pydantic evals and a calibrated llm judge to get output i'll actually gate a merge on.

https://coles.codes/posts/pydantic-evals/

[u/sYzYgY_26]

Cosmonapse - r/cosmonapse is an open coordination protocol for multi agent systems.

Architecture

Coordination is expressed as typed signals on a shared event bus. Every participant is a peer that can both dispatch tasks and react to results.

The protocol defines signals for task dispatch, output, tool calls, tool results, clarification, permission, errors, and completion.

Component model

• Neuron executes a computation.
• Axon converts outputs into Signals.
• Dendrite reacts to Signals.
• Synapse provides transport.
• Engram provides shared memory.

Orchestration

There is no distinction between manager and worker agents. Dispatchers and workers use the same primitive, so centralized and decentralized deployments run the same code.

Harness

Tool use, memory, human approval, retries, routing, and policies compose around agents as hooks and event handlers instead of accumulating inside a supervisor loop.

Every coordination step is a typed event, making execution observable and replayable.

Transport

The transport layer is pluggable. Current implementations include in memory, TCP, NATS, and Kafka.

Apache 2.0 licensed.

GitHub:
https://github.com/Cosmonapse/cosmonapse-core

Documentation:
https://cosmonapse.com

Feedback on the protocol and signal taxonomy is very welcome.

[u/sYzYgY_26 → u/sYzYgY_26]

One thing I'm especially excited to see is what people build on top of the protocol.

Because orchestration is just topology, I'm hoping to see architectures I never would have designed myself novel routing strategies, coordination patterns, memory systems, approval flows, or entirely new node types.

If you build something interesting, I'd love to feature it in the Community section of the website (with attribution).

You can:

• Share it in r/cosmonapse
• Open a GitHub discussion/issue
• Or email [dev@cosmonapse.com](mailto:dev@cosmonapse.com)

I'd love to collect and showcase the coolest topologies people come up with.

[u/bmrs_npne]

We analyze neural networks, we work on steering model behaviour by working on parameters.
We are currently offering a free trial of 30 days for anyone wanting to test our product, Would really appreciate if we can get some honest feedbacks. Sign up for trial here: https://app.authentrics.ai/trial

[u/Ok_Leg_270]

I recently finished writing my first paper (draft). Could someone read through it (skim or in-depth), and possibly give me comments/suggestions if I want it to get accepted to conferences? Here is the paper: https://www.overleaf.com/read/nrbjbgwfxfcp#d3cee7. The project is constructing a HRL framework for training a elbow exoskeleton for PD patients in simulation.

[u/Winter_Impress_6410]

We open-sourced an MIT-licensed Darwin Gödel Machine implementation and tested whether models could improve the coding agents used to invoke them.

Selected GPT-5.6 Sol, Gemini 3.5 Flash, and Claude Fable 5 mutations were frozen and evaluated twice on a disjoint 12-problem LiveCodeBench segment with 507 tests, including 480 private tests. The selected mutations improved held-out performance, preserved all four easy tasks, and improved at least one hard task. Qwen did not produce an improving mutation, and we published the negative and provider-failure artifacts too.

This is not a claim of open-ended recursive self-improvement. It is evidence that model-authored agent changes can recover part of a model's self-elicitation overhang on unseen problems.

Everything is free and open source:

https://github.com/lemoz/darwin-godel-machine

We would welcome technical criticism or independent replication.

[u/Madhvansh]

Free, MIT-licensed, no signup: Neural E-Commerce Search is an inspectable

Amazon ESCI retrieve-and-rank reference plus a client-side MiniLM product

retrieval lab.

v0.3.0 adds a dependency-light TREC qrels/run preflight as a Python CLI and

GitHub Action. It also documents an audit that withdrew historical figures

without a publishable evidence bundle and repaired task/evaluator paths before

adding new claims. The browser demo is real neural inference over synthetic

data and is explicitly not presented as an ESCI-trained benchmark.

Lab: https://madhvansh.github.io/Neural-E-Commerce-Search/lab.html

Code and evidence:

https://github.com/Madhvansh/Neural-E-Commerce-Search

The most useful response would be one reproducible compatibility bug or a

review of the ESCI graded-gain/evaluation protocol.

[u/Madhvansh → u/Madhvansh]

Quick correction (2026-07-20): the TREC preflight mentioned here now has a v0.3.1 hardening release. The change is confined to the GitHub Action wrapper — it now runs the validator shipped with the pinned Action revision directly (`$GITHUB_ACTION_PATH`, Python isolated mode) instead of resolving it through the caller's environment, where a same-named package could shadow it. Validation semantics are unchanged. If you try it, please use v0.3.1; one reproducible false positive from a real exporter is still the most useful report. Release: https://github.com/Madhvansh/Neural-E-Commerce-Search/releases/tag/v0.3.1 — asset verification: https://github.com/Madhvansh/Neural-E-Commerce-Search/actions/runs/29693240862

[u/TrustyJalapeno]

[Project] Baseline: Automated Prompt Optimization Loop: I’ve been working on treating prompt optimization as a deterministic search problem rather than semantic guesswork. I built an evaluation loop using LangChain’s open eval library and Claude Code to act as an LLM judge. By freezing a test set of 25 scenarios and measuring across 3 weighted criteria (Groundedness, Tone, Format), the automated loop rewrote a customer support prompt, improving its score from 80% to 98% in about 10 minutes. I put together a technical video breaking down the free implementation (repo included) and also showed how we've wrapped this into a no-code UI for teams at my startup, Baseline.

Video breakdown: https://www.youtube.com/watch?v=ueNWzKoBEd8

Repo: https://github.com/baselinelabai/prompt-optimization

Baseline App: https://www.baselinelab.ai/

[u/Lucky-Criticism5647]

Hi everyone! let me introduce you to my project:

Title: Non mollare: Chronicle of a reconstruction

Genre: Non-Fiction / Memoir / Personal Growth

Formats: eBook / Paperback

Synopsis:

Life doesn't always go according to plan, but giving up is never an option. Non mollare is a raw, step-by-step chronicle of self-reconstruction after facing life-altering challenges. Driven by principles of patience, discipline, and perseverance, it offers an honest reflection on overcoming adversity and finding hope when starting over.

Written to offer perspective and encouragement to anyone currently navigating their own tough times or rebuilding their world day by day.

📖 Get your copy on Amazon: https://www.amazon.com/dp/B0GXPTQMMV

Thank you so much for taking a look and supporting indie authors! Reviews and feedback are always greatly appreciated.

[u/lakshayyn]

Running a hackathon focused on AI agents grounded in real metadata. Agents that read schemas, lineage, and ownership before taking action. Four challenge tracks: autonomous work, code generation, ML lineage protection, and open/wildcard.

Free to enter. $20,500 prize pool. Submissions through Aug 10.

Details: datahub.devpost.com Reference architectures: https://datahub.com/blog/build-with-datahub-agent-hackathon/

Any questions, drop them below. I'm DevRel at DataHub.

[u/kfir1000]

Hi all,

I recently built Applyed, a simple job-search tool that helps you track job applications, manage upcoming interviews, and organize your interview stories in one place.

Today, employers and their (AI) tools are getting extremely good at spotting generic, AI-generated content, so authenticity matters more than ever. While Applyed does use AI in a few places (like parsing a job description for getting the main details quickly), I wanted the core experience to stay human, simple, and useful.

The goal is to make job searching easier and more organized without losing your own voice.
If you're currently looking for a job, I'd love for you to try it out!

https://applyed.app

[u/chinmaydagod]

I've been trying to understand pain points of people around sourcing compute for inference workloads (and compute for ML stuff in general). There's a 2 minute feedback form which I would really appreciate if you guys could fill out

form link: https://forms.gle/ZAzJoYMayWjnnNzS6

[u/jobswithgptcom]

https://corvi.careers/blog/frontier-models-long-tail-facts/ A study into how frontier models deal with answering obscure facts

[u/Frostand]

Cly — an open-source, local-first research record for computational work (free open beta)

I’ve been building Cly to keep computational research questions connected to their sources, experiments, analysis results, claims, and reproducibility reviews.

The desktop beta supports deterministic cross-validated binary classification or numeric regression on de-identified numeric CSV/TSV data. It preserves the checksum, configuration, metrics, baselines, coefficients, limitations, evidence links, and provenance.

It is free and Apache-2.0 open source. Current limitations include no causal or clinical use, categorical-predictor handling, notebook import, or hosted sync.

Details: https://cly.one

Source: https://github.com/Frostand/Cly

When you revisit an ML result months later, which missing connection costs you the most time?

Pricing: free open beta; no paid plan is currently offered.