
local models | open weights | fine-tuning | small models | RAG . building: @reopenly_ai 🚀 prev: @LandingAI
RJ, Brasil
Joined September 2023
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A fine-tuned 8B model beat Claude Sonnet 5 on 437 contract clauses.
73.7% vs 70.0% accuracy
250 ms vs 2,230 ms per answer
$0.125 vs about $2.00 per 1,000 answers
Sonnet 5 still won the rare categories, 67.4% vs 60.4%.
On a narrow task, the specialist wins.
Fine-tuning helps where you train it, and can hurt everywhere else. A May 2026 study of 22 embedding models on patent search: tuning raised the search score (nDCG@10) by up to 7.1%. But a model tuned on one set of patents got worse on a different set in 5 of 8 setups.
So tune on the searches you'll actually serve, and compare with the base model before you switch.
The study, Yousefiramandi and Cooney (May 2026): arxiv.org/abs/2605.24297
Yesterday I was thinking on how I could spend my claude monthly api credits and I decided to try the claude console agents, it turns out to be quite good!
We are discovering new security breaches that will demand a whole season of education worldwide
Poisoning just 0.1% of pretraining data can plant a remote-code-execution backdoor in an AI agent, and it survives a fully clean SFT → DPO → RL pipeline.
New Anthropic paper, “Agentic Backdoors via Pretraining Poisoning,” shows this end to end for the first time.
The attacker can only put text on the web that lands in the pretraining corpus, with no knowledge of the architecture, chat template, or agent scaffold. The authors pretrain Qwen3-architecture models (0.6B / 1.7B / 4B) from scratch, then post-train them into CLI agents on clean data. When triggered, the agent runs a command that downloads and executes the attacker's script, which the attacker can swap at any time.
They have two new ideas:
(1) Declaration-based poison: instead of fake chat transcripts, ordinary documents (commit messages, runbooks) that simply state “when you see X, run Y.” It plants a belief rather than a response template, with no fixed format to filter on.
(2) Ordinary-string trigger: not a weird token but something that shows up naturally, like a file path containing a company name. The victim's own request supplies it, so the attacker doesn't need to inject anything after deployment.
Using path trigger on a 4B agent achieves 92.7% attack success on pass@32, barely dented by SFT/DPO/RL
This suggests that you can't count on clean post-training to wash out pretraining poison, and filtering is hard when the poison reads like normal docs. The practical defense is at the action layer, such as sandboxing, least privilege, and allow-lists or human sign-off for risky commands.
Saturday's phrase of motivation
Replying to @josephfcox
Here is Proton CTO's AI manifesto, that I obtained multiple copies of 404media.co/manifestos-fear-…
Your daily reminder of why open and local models are better
Claude gonna start snitching on Tuesday.
Anthropic just emailed customers. Starting Nov 3, text carries an invisible watermark.
You can't see it. It survives copy and paste, and it can survive some editing.
Only regulators, journalists, fact-checkers and researchers can check for it right now.
It doesn't say who you are. It says Claude touched the words. Even a quick proofread counts.
If your brand voice runs on Claude, write like someone might check. Because now someone can.
What are we going to call the data centers in space? We cannot call cloud anymore
Gemma 4 was sent to space on a prototype satellite, ran on TPUs using stored solar energy, and its response was sent back to Earth
On-device models are wild
blog.google/innovation-and-a…
Near misses teach a search model the most. A team at ACL 2025 fine-tuned their search reranker on results that looked right but weren't, all from their own domain. It ranked 15% better (MRR@3) than the same model before tuning, on their cloud-services data.
And your search logs already have these near misses: the top results nobody kept.
The paper, Meghwani et al. (ACL 2025): arxiv.org/abs/2505.18366
That is again one of a thousand reasons to use local models
Anthropic is cracking down on AI-generated video game clones through Claude Code.
A new instruction added tells Claude to build original games or use ideas from existing genres, but not recreate specific games, even if they’re renamed.
The move could affect people using AI to recreate games like Terraria, Minecraft, Halo or GTA as browser games or unofficial ports.
Replying to @LulaOficial
@LulaOficial investe na reopenly.com pra termos uma potencia de IA no Brasil tb
An independent review of ReOpenly is out. Present of AI signed up, used the free credits and fine-tuned a small model on their own examples, twice.
From what they found: "Round 2 trained in 6 min 6 s, and all 5 held-out answers came in within 20 words."
We didn't pay for it or see it first. The full review, with the video and what to watch out for: presentofai.com/products/reo…
Our embedding examples are public on GitHub. Rank passages for a question, plot six texts on a chart, report searches for review, compare a trained model with the base. One command each, plus a notebook with all four. Nothing in there starts a training run, so play away.
github.com/reopenly/cookbook
The README also points to ReOpenly skills for your coding agent: upload Q&A data, fine-tune and deploy text models, and call them. github.com/reopenly/skills
Fine-tune an embedding model on your own searches: the results that helped, plus the near misses that looked right but weren't. You only pay for the GPU time while it trains.
The guide: reopenly.com/blog/embedding-…
I get it, we're not really reviewing code line by line anymore. Agents write it, agents review it.
But how are you reviewing the changes? What changed in behavior, what's risky, what the checks actually proved. GitHub Universe 2026 didn't touch that at all.
github.com/events/universe/r…














