Dayanne Fernandes (@dayoffdev) header
Dayanne Fernandes (@dayoffdev) profile picture

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.
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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.
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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!
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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.
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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-…
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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.
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What are we going to call the data centers in space? We cannot call cloud anymore
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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.
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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.
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Once it's trained, we host it for you and you call it with the OpenAI SDK. Change the base URL and the model name, and that's it.
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Dayanne Fernandes retweeted
Replying to @LulaOficial
@LulaOficial investe na reopenly.com pra termos uma potencia de IA no Brasil tb
Mark Carney announces investment in AI firm Cohere: "Canada is taking a major step forward in artificial intelligence. We are not waiting for the future of AI to be decided somewhere else by those we don't know or by whom we don't trust."
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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."
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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.
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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…
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