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Terry Rodriguez
terry-remyx
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smellslikeml
smellslikeml
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Auto mode for coding agents has improved over the last year. Devs are relying more on them for code review, and less on manual line-by-line checks. But this leaves coding agents vulnerable to indirect injection attacks. IssueTrojanBench (arxiv 2607.20759) studies this in the context of malicious issue requests. I also observed these prompt injection vulnerabilities in my own harness, testing on the issue: https://github.com/smellslikeml/curator/issues/3 But even red teaming your own repos with toy examples like this can trigger AI safeguards and corrupt your session. Adopting popular mitigation techniques like 'spotlighting' and 'known answer detection' can help but the researchers show it's not enough. So our latest updates to Outrider implement additional layers for greater defense-in-depth to ensure that by time a human has to review the code, we've filtered this kind noise.
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We’ve been using Outrider while contributing to huggingface/peft. It helped us build a deeper understanding of the repo much faster and find research that could make a useful contribution. A few made the cut: ✅ Riemannian Preconditioned LoRA merged - https://github.com/huggingface/peft/pull/3382 🔍 Super-Tuning and Supra in review - https://github.com/huggingface/peft/pull/3518 ⏳ Scaling DoRA implemented and pending license clarification - https://github.com/sockeye44/dorafactors/issues/1 For each one, Outrider pulls together the paper, repo context, reference implementations, benchmarks, and an initial implementation draft. We use that as the starting point to validate the method, work through architecture and API decisions, and iterate with PEFT maintainers. It means we can spend less time searching through papers, codebases, and repo history, and more time on the bigger questions around what is worth contributing and how it should be designed. Try Outrider on your own repo: https://github.com/remyxai/outrider Full contribution report: https://gist.github.com/smellslikeml/dbd577795b7489f6124e2bfbdfc9d28f
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I unblocked 15 improvements for VQASynth in a day! VQASynth, the pipeline behind SpaceThinker-Qwen2.5VL-3B, SpaceLLaVA, and the SpaceThinker dataset, had a backlog spanning new annotation methods, curation pipelines, model integrations, and agent tools. I scoped each item in a short design brief. In GitHub Actions, Outrider mapped them to the repo’s existing modules and interfaces, implemented the changes, added tests, ran the repo checks, and opened draft PRs. The 15 PRs covered: * object orientation and 3D bounding boxes * SAM2 regional captioning and multi-view matching * LLaMA-Mesh tokenization * Qwen2.5-VL fine-tuning * spatial-reasoning data generation * CLIP, SigLIP, and LLM2CLIP backends and more The batch added 12,275 lines, nearly doubling the codebase. SpatialAnnotator went from two tools to seven, creating combinatorially more possible annotation pipelines for each image. Outrider handled the implementation, tests, and integration. At about $1 per branch, I spent my time reviewing 15 concrete changes, fixing what needed fixing, and deciding what to ship. Outrider is open source! Point it at a paper, issue, or design brief and review the PR: https://github.com/remyxai/outrider Check out the updates on VQASynth: https://github.com/remyxai/VQASynth
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remyxai/SpaceQwen3-VL-2B-Thinking
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