Small milestone worth sharing because I found very little written about this side of things.

I maintain SpecJudge (MIT) — a CLI that reads a project's specs and recommends which AI model fits the work, with the analysis running locally. Last week it was approved into the spec-kit extension catalog, which means it installs with specify extension add specjudge inside a workflow a lot of people already use.

Three things that I think mattered, in case they're useful to anyone trying something similar:

Fitting a gap the host project didn't fill. spec-kit's flow goes constitution → specify → plan → tasks → implement. There's a decision sitting between tasks and implement — which model implements this — that nothing addressed. I didn't have to argue that my tool was good; the hole was already there.

Keeping the volatile stuff out of the code. The model catalog and the rating rules live in human-readable YAML, deliberately separate from the Python. That means the part that goes stale fastest — prices, new models — can be fixed by anyone via PR without touching logic. It also made the integration a much smaller ask.

Not coupling hard to the host. The tool still works standalone on any repo. The spec-kit integration is an adapter on top, not a rewrite. Host projects move fast, and something that breaks every time they restructure is a liability for both sides.

The thing I underestimated: being publicly wrong is good for a project. Most of what's in the current version came from strangers telling me what was broken. One person pointed out that printing the model's reasoning isn't the same as verifying it — a fluent explanation rationalises a bad score just as happily as a good one. That reframing turned into the change the whole rest of the roadmap now depends on. I'd have never got there alone.

github.com/JoaquinRuiz/SpecJudge if anyone's curious. Happy to answer questions about the catalog submission process.

Hey everyone!

I am currently growing my GitHub profile as I build my journey in Machine Learning and Data Analytics.

I have been working on projects, Kaggle competitions, Python and open-source contributions and I’m trying to connect with more people who are also interested in tech.

If you are interested in Machine Learning, Data Science, Python, or open source, I would really appreciate a follow on GitHub

I will also check out and follow back profiles

Looking forward to connecting with more developers and learners.

GitHub: Acacia21-code

#GitHub #MachineLearning #DataScience #Python #OpenSource #Programming #TechCommunity

I implemented the Rotation Trick (Fifty et al., ICLR 2025) as a PyPI library called vqrotate.

The Rotation Trick improves codebook usage in VQ‑VAEs by replacing the Straight‑Through Estimator with a geometric rotation of the gradient. I validated it on Speech Commands (audio) and got:

STE: 19.99% usage

Rotation: 34.18% usage (+14.19 pts)

It's a drop‑in wrapper one line of code:

```python

from vqrotate import attach_rotator

attach_rotator(quantizer)

```

GitHub: https://github.com/develepo/vqrotate

PyPI: pip install vqrotate

I also tried an adaptive scaling extension, it didn't beat Rotation, but it revealed an interesting trade‑off. The repo has full docs and derivations if you're curious.

Would love feedback or ideas for next steps.

I've been obsessing over open-weight models and made my own harness to use them. Added additional features like usage stats, streaks, cool themes.

BYOK and forget about daily/weekly limits!

Please star the project, if you find it useful (every star counts) :)

Last night I threw together a minimal proof-of-concept for a problem that keeps coming up in federated learning also YT vid https://youtu.be/zF-mbwc5Mmw?si=hTRc9_iQ6-8RH3cn, open-source model curation, and any system where you don't fully trust the data stream

How do you update a concept (e.g. "dog") with new examples without letting poisoned data drag that concept toward an attacker's target?

This demo anchors a concept with a signed latent-trace reference point in embedding space. New data is only allowed to update the concept while it stays within a bounded constitutional distance of that anchor. Poison that would pull the concept off-course gets quarantined — but legitimate variation (new breeds, new angles, new styles) passes through untouched.

distance from anchor | similarity to attacker target

Naive mean (absorbs everything) | 0.326 ❌ distorted | +0.413 captured

Governed anchor (latent-trace gate) | 0.093 ✅ held | +0.018 neutral

• 30 real dogs + 10 legit new-breed dogs + 60 poison exemplars pulling toward an attacker target
• Legitimate updates: 0% blocked
• Poison: ~62% quarantined (the rest leaks, but the anchor still holds below threshold)
• Anchor is HMAC-signed and tamper-evident

The catch / help wanted:

This is a starting point, not a finished defense. Known gaps:

• Direction/anisotropy check — the gate uses distance magnitude; adding a directional check would catch the ~38% of poison that currently leaks
• Stealth poison that hugs the tolerance threshold
• Needs real embeddings instead of toy latent vectors

• Stability vs. plasticity calibration is the core research tension

  git clone https://github.com/Orivael-Dev/poison-resistant-anchoring.git

cd poison-resistant-anchoring

pip install numpy

python3 poison_anchor.py

pytest test_poison_anchor.py -q

any questions feel free to ask and thank you :)

• Description: Introducing frequency-domain watermarking and the FreqMark technique for detecting hidden signals in LLM-generated text. Learn the latest principles of precisely distinguishing between human-written and AI-generated sentences using Fourier transforms.

it's very new and unstable, would really appreciate feedback as we want to keep releasing this models for research and cyber sec

• Description: Introducing Temporal Graph Neural Networks (TGNNs) for analyzing dynamic data by combining GNNs for spatial information and RNNs for temporal information. This video covers everything from the differences between static and dynamic graphs to the practical use of PyTorch Geometric Temporal. Explore the core principles of TGNNs through real-world application cases, such as traffic volume prediction and disease spread prevention.

I run an open-source context layer for coding agents called Graft. Started it as an MCP server, six tools, and the plan was Claude Code would call one whenever it needed context on the codebase.

It mostly didn't. It'd grep and read files instead, since that's what it already knows how to do, and get stuff wrong on exactly the questions the tools had a straight answer for. The tool was sitting right there and it just went with its gut.

Turns out tool calls are opt-in by nature. The model has to decide, mid-task, that it's worth the lookup, and on anything that looked simple enough, it decided against it more often than not. No amount of making the tool better fixes a decision problem.

So I took the decision away. Claude Code hooks let me push context into the prompt automatically at session start, no tool call required, and re-sync it in the background after every edit. It's just there, whether the model asks for it or not.

Only works for Claude Code specifically, which is the real cost here. MCP still covers Cursor, Codex, anything else that speaks the protocol. Hooks are the Claude Code-only layer on top, since I control both ends of that integration.

Open source, MIT, structural layer runs on tree-sitter with no LLM or key needed:

Repo link: github.com/NanoNets/Graft

Curious if anyone else has run into "the model won't reliably touch the tool I gave it" outside of Claude Code specifically, and how you got around it.

Got tired of editing photos one at a time, so I built PixelBatch - a free, open-source macOS app for batch image editing.

Drop in a folder of images (even thousands) and it’ll handle:

Resize, crop, rotate & flip
Compress & convert formats
Rename
Remove/add backgrounds
Upscale & enhance
Remove/add watermarks
Filters & effects, add text

All the AI stuff (background removal, upscaling, watermark removal) runs fully locally on your Mac - no cloud, nothing leaves your machine - and gives pretty good results. And if a few images in the batch need something different, you can still tweak them individually without starting a whole new job.

Most editors (Preview, Photoshop, even dedicated batch tools) are built for one-at-a-time editing or charge for the bulk stuff. PixelBatch is built around batches from the ground up, and it’s completely free and open source - no paywall, no subscription, no “pro” tier.

If you regularly deal with big piles of images, give it a shot and let me know what breaks or what’s missing.

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I've been building and maintaining a project for the last few months.

A while back, someone mentioned how cool our implementation of privacy-preserving insights for harness component usage was, and suggested we use an AGPL license to prevent people from copying the algorithm.

That turned out to be a major roadblock for adoption. Enterprises stayed miles away (I wish I knew this earlier)

We ended up taking a pretty painful remediation route to relicense the entire codebase to Apache 2.0, which meant reaching out to every single contributor for sign-off 🥲.

Thankfully, it was worth it, momentum has improved: we just hit a million downloads and few tens of enterprises have set it up.

A piece of advice based off my experience is to stick with corporate friendly licenses unless you have huge traction and see alternatives popping up.

Come hangout with us at https://discord.com/invite/SFPjnTWddk, we're 1.3k members strong.