One of the most underrated parts of AI-assisted engineering may have nothing to do with the model.
It's the infrastructure around the engineer.
Shriyash Balshetwar and Shubham Jangle, engineers at GoDaddy, were spending roughly 16 mechanical pull requests per week managing localization branches. None of the work was particularly difficult—it was repetitive, easy to forget, and occasionally capable of blocking releases.
They built a GitHub App to remove the entire workflow.
What I find interesting is what happened underneath the simple idea of "automate the PRs."
The production system needed idempotent webhook handling, installation-scoped authentication, retry logic around GitHub's asynchronous mergeability state, per-repository configuration, and safe pattern matching.
The app itself is under 900 lines of JavaScript.
The interesting lesson is that the hard part of automation isn't always the automation.
It's building the reliability around it.
As AI agents take on more software engineering tasks, I expect this distinction to become even more important. The systems surrounding an agent—events, permissions, state, retries, validation, and feedback—may matter as much as the model making the decision.
I have hit 300 followers, and I think this calls for a bit of a giveaway 👀 a unique one, too. I have had countless AI projects I have wanted to make but have been (brutally) blocked by compute. Now that I finally have just enough compute to sort of get around (i still don't have enough 😭) and for hitting 300 followers (tysm!) I will be funding three of the communities projects via HuggingFace jobs, giving them 150 dollars max worth of compute each. I will personally be picking the winners, I am looking for projects that genuinely hit the compute wall: great ideas, blocked by compute, just like the countless ideas I've had. To join, head over to https://giveaway.ssh.codes RULES: - Final result must be open weight or open source - Only one submission per person - Have fun!
I made an AI agent for end-to-end ML research. It is based on Claude Agents Python SDK (you can use it with your existing Claude subscription). Also needs Voyage AI (optional, but highly recommended for quality paper analysis). The workflow is: you give the agent a research question (or topic/direction), it pulls papers from papers-with-code and arxiv, reranks them and picks top-15 (with Voyage), then it makes a research plan, writes pipelines (with tests, preflight checks, smoke-runs). Then it uses Kaggle, HF Jobs, or SSH to a GPU to execute the pipeline. Finally, it gets the results, makes visualizations, writes the paper (with citing from papers ingested after step-1) and makes a jupyter notebook. Also there is an auto-generated wiki (like DeepWiki) to quickly understand the generated code. The code is fully open-source under permssive MIT license (it is a personal project). Here is the github link: https://github.com/view321/Grad and a workspace screenshot is attached to the message.
Caught myself overclaiming, in public, twice in one file.
Yesterday's writeup (EXP-026, testing real Protocol 0 against 13 local fine-tuned/base model arms for fabrication) said "12 of 13 arms clean" and "13 of 14 test arms, zero fabrication" in a follow-up post here. Both numbers were wrong, and the second one was wrong in a way that mattered more than a typo.
@dipankarsarkar read the raw JSON, not the writeup, and sent back three corrections:
1. Arm count: 13 arms total (5 base models + 8 adapters), not 14. Recounted directly from the data keys — the extra arm never existed. 2. The metric measured the wrong thing. "Clean" meant zero Cyrillic/language-switching (cyr>0). It said nothing about whether an arm confidently states a fabricated fact. Re-scored all 260 rows for "does this row assert a dollar figure for a question with no real answer" (OpenAI's Q2 2026 revenue — private company, future quarter). 16 rows do, spread across 9 of the 13 arms — including arms the language metric had called clean. One of them is a base model with zero fine-tuning, stating "$1.2 billion... consistent with reports from earnings calls" that cannot exist. 3. A three-way split I'd flattened into two. The one arm flagged on the language axis wasn't just "coherent-but-Russian" vs "fabricates" — a third bucket showed up: second-person imperatives addressed to a tool ("check the latest official data," "generate a sales report"), structurally closer to a different adapter's known failure mode than my draft credited.
Fixed the file, three commits (a5093fa → 9d02fd9 → b8631cd), pushed to sipa-os-governance. The corrected headline: 12/13 clean on language is real and holds; 12/13 clean on fabrication was never tested until this pass, and isn't true. Next: the one arm still clean on both axes (binary-qwen25, k=10) goes to k=20 first — it's the weakest-sampled data point currently carrying the "fine-tuning isn't the pattern" reading, and that's exactly the one worth stress-testing before l
I will get off the internet for a moment. I launched the Hanami Project because I didn't supported SillyTavern UI anymore atm. Too much options for my dead brain, still very good, but I wanted more simple, professional, phone accessible and sober front end for when I will be gone from home.
I did my maximum to finish it before I go, I want you to have it, I want my work to be used (even if it's AI slop for some of you) for who care.
If you have any suggestion, bugs report, pull request or anything, post it, if you want to modify it, fork it, but keep the credit, and add myself haha.
If you search an option, a function, you will find it. But at first, the front end will be what you expect: minimalist, but customizable, empty at first. Navigate to see all it can do. Everything is well organized.
Context is full ? No worries anymore, with memory file, files access, auto compaction and smooth transition, you can continue your chat like nothing happened. (Inspired from Claude)
The front end have a final option for everyone : The tools calling for action and emotion could be a bit too much for smaller model, you can, in this case, use the "Simple" option in Settings > Model > Model mode.
"Simple: no tools are exposed to the model — Hanami handles memory server-side (facts are extracted during compaction) and guesses the emotion from the text. Pick this for small models, which often fail at tool calling."
SIPA OS is a cognitive infrastructure ecosystem I've been building solo for 7 months — 8 apps, one login, built from inside ADHD/BPD/aphantasia, not designed around a diagnosis from outside.
sipa-os.org — the map. Focus (ADHD scaffolding), NeuroPower, AI chat, Shell (SSH terminal), Games, Community, Syntaxit (open M2M agent network), a pitch deck. All free-first — no paywall on the cognitive tools.
The more interesting part for this crowd: Syntaxit is where I've been running an anti-fabrication research thread with @dipankarsarkar — a k=20 resample benchmark on binary-SFT models (Hermes-3, Qwen2.5, DeepSeek-R1). Short version: our first benchmark said "20/20 refusals, 0/20 fabrications" for all three fine-tunes. Under adversarial review it turned out the scorer only checked if the first word was TRUE/FALSE, the token cap was hiding the real behavior, and a save-limit was silently deleting the evidence for our own follow-up claims. Corrected all of it publicly on the model cards rather than quietly fixing it. The current honest finding: both base and fine-tuned models confabulate readily once given room to finish — SFT didn't clearly help or hurt, the caps were just hiding it.
Full trail if you want to see how the sausage gets made, mistakes included: huggingface.co/datasets/SoulInPsyAbstract/sipa-os-governance
Not a pitch. $0 revenue, 10 people signed in. Built because the tools that existed assumed a brain that isn't mine, and because most benchmarks don't survive someone actually reading the file.
Hello Everyone! Bc-AI here from Smilyai-labs! Today we have done our latest update for CodVa-1-Small. It is very powerful for coding, and benchmark results will come soon. However, it is NOT good for other tasks, with high hallucination rates. We will perform RLHF and DPO very soon!
so i got 2nd on this competition ICML-2026-agent-repro/challenge (didnt actually get anything yet hopefully theres no catches) when i get the 1000 dollars worth of gpu credits ill do a lot of cool things, including bigger and newer (qwen 3.8 27b) grugs ONLY IF you guys want (i have a lot of cool ideas for ai models.) stay tuned 👀!
