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Terry Rodriguez
terry-remyx
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reacted to salma-remyx's post with 🚀 2 days ago
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 reacted to salma-remyx's post with ❤️ 6 days ago
The evidence that gets a method published usually isn't the evidence you need to decide whether to ship it in your project.
We've been building Validate in Remyx to surface the evidence devs & maintainers need to make that call.
Within GitHub and our studio you can:
* define your project's validation configuration
* implement & run it against a proposed implementation
* review the results alongside the diffs
We're looking for a small group of engineers actively integrating new AI methods into production to help shape Validate. If that sounds like your team, reach out!
LI: https://www.linkedin.com/in/smayorquin/
email me: salma@remyx.ai
reacted to TravisMuhlestein's post with 👀 6 days ago
One of the more interesting questions in AI-assisted software development is whether increasingly detailed system prompts actually improve code quality.
Johnathen Chilcher (Senior SRE at GoDaddy) explored that question through a large-scale benchmarking effort.
What started as 1,458 Python benchmarks has grown into 212,000+ controlled evaluations across Python, Go, JavaScript, and C#, using Claude Haiku, Sonnet, and Opus.
Several findings stood out:
Across the original Python benchmarks, no prompt configuration consistently outperformed an empty prompt.
Generic instructions such as "write clean code" or "follow best practices" often reduced performance rather than improving it.
The information that consistently helped wasn't generic advice—it was project-specific context the model couldn't infer from training, including repository structure, build commands, coding conventions, and the current state of the codebase.
Chain-of-thought prompting helped in Go and C#, but hurt performance in Python.
Prompt tone mattered: encouraging language generally outperformed high-pressure or urgent wording.
The effectiveness of prompting techniques varied across programming languages and models.
One aspect I found particularly compelling is that the conclusions evolved as the benchmark grew. Earlier recommendations changed as additional data became available—a good reminder that empirical evaluation matters more than intuition.
The broader implication is that we're moving beyond prompt engineering toward designing systems that automatically provide AI coding agents with the context they actually need to succeed.
📄 Full article:
https://www.godaddy.com/resources/news/what-an-effective-ai-coding-prompt-looks-like
I'd be interested to hear whether others have observed similar patterns across GPT, Gemini, Llama, DeepSeek, or other coding models. Organizations
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