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wsxiaoys

Luma/Meshy are generative models that output a high-fidelity mesh in one shot. This is more about testing an LLM’s spatial and visual understanding: the agent writes the geometry code and has to reason about the 3D result from what it sees, then iterate.

Show HN: A 3D mesh playground for coding agents (three.js + manifold-3d)

https://sketchagent.dev/s/yfYU6LhT

When GPT-6/Gemini launched recently, a lot of the 3D demos used blender-mcp (https://github.com/ahujasid/blender-mcp). It's cool, but you have to install blender locally and wire up plugins just to try a prompt or share a run.I built a browser-based 3D mesh playground so you can hook up your own coding agent over MCP (or run in-browser) directly in a tab. It runs on three.js + manifold-3d for...

( Read More... | 2 comments | on HN )
Re:Qwen 3.8 follows GPT-5.5 Pro reasoning prefills on Wednesday September 09, 2026 @02:16PM (#49630949)
Appendix B of https://stolen-thoughts.com/paper.pdf discusses this

Reasoning prefills on a few open models

https://gist.github.com/wsxiaoys/102e8654c14d5d27b7b77532026ebfa5

I wrapped this small followup to stolen-thoughts.com in a gist for easier reading and wanted to share it here.My hunch is that this might not just be reasoning distillation; it could even be benchmark distillation. This is a small experiment and definitely doesn’t prove anything about how any model was trained, but I thought the contrast was interesting enough to share.I’d also like to try...

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> Would be interesting to know how much a jj-specific SKILL.md would race the score.

That is definitely something we're interested in; we will try running this evaluation with skills soon.

> This might not fit the evaluation framework, but I'd still be interested in your experience/setup with terminal-based coding agents like Claude Code.

We have adopted Harbor as our evaluation framework, so evaluating Claude Code is straightforward: https://harborframework.com/docs/agents#installed-agents

Show HN: jj-benchmark – Evaluating AI agents on Jujutsu version control

https://tabbyml.github.io/jj-benchmark/

Hi HN, Meng from TabbyML here.We decided to build this simply because we find Jujutsu (jj) really interesting, and many folks on our team have started trying it out recently. Since it introduces a very different workflow compared to traditional Git, we thought it would be a fun challenge to see how well current AI coding agents can actually use it.To build this, we created a semi-automated...

( Read More... | 2 comments | 4 points | on HN )
Hi HN,

I wrote a 4-part series on how we built the AI edit model behind Pochi’s coding agent.

It covers everything from real-time context management and request lifecycles to dynamically rendering code edits using only VS Code’s public APIs.

I’ve written this as openly and concretely as possible, with implementation details and trade-offs.

Full series:

1. The Edit Model Behind Tab Completion: https://docs.getpochi.com/developer-updates/how-we-created-n...

2. Real-Time Context Management in Your Code Editor: https://docs.getpochi.com/developer-updates/context-manageme...

3. Request Management Under Continuous Typing: https://docs.getpochi.com/developer-updates/request-manageme...

4. Dynamic Rendering Strategies for AI Code Edits: https://docs.getpochi.com/developer-updates/dynamic-renderin...

Re:Working Around VS Code APIs to Render LLM Suggestions on Thursday January 08, 2026 @12:27PM (#46543742)
OP here - I've talked in detail about how we rendered NES suggestions using only VS Code public APIs.

Most tools fork the editor or build a custom IDE so they can skip the hard interaction problems.

Our NES is a VS Code–native feature. That meant living inside strict performance budgets and interaction patterns that were never designed for LLMs proposing multi-line, structural edits in real time.

In this case, surfacing enough context for an AI suggestion to be actionable, without stealing attention, is much harder.

That pushed us toward a dynamic rendering strategy instead of a single AI suggestion UI. Each path gets deliberately scoped to the situations where it performs best, aligning it with the least disruptive representation for a given edit.

If AI is going to live inside real editors, I think this is the layer that actually matters.

Happy to hear your thoughts!

OP here - happy to answer any questions.

This was one of the more unexpectedly tricky layers of building real-time LLM suggestions, and I’d love to hear how others have approached timing, cancellation, and speculative prediction in their editors or agents.

Ask HN: Change my mind) should AI coding conversations be append-only?

I work on an open-source coding agent, and one design choice we made is to keep conversations append-only. No editing past prompts.Many would argue this feels restrictive. Isn’t it just better to edit an earlier message or tweak the prompt and move on? And this is what most coding agents allow, and on the front, it looks convenient. But realistically, editing conversation history throws away...

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Ask HN: Do coding agents optimize the wrong review step

This is a personal opinion, but I think current coding agents requires human reviews AI's artficats at the wrong moment. Most tools focus on creating and reviewing the plan before execution.So the idea behind this is to approve intent before letting the agent touch the codebase. That sounds reasonable, but in practice, it’s not where the real learning happens.The "plan mode" takes place before...

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Re:How we built context management for tab completion on Monday December 08, 2025 @09:59AM (#46192947)
OP here - this is Part 2 of a series documenting how we built NES (Next Edit Suggestions), our real-time edit model inside the Pochi editor extension.

The real challenge (and what ultimately determines whether NES feels “intent-aware”) was how we manage context in real time while the developer is editing live. For anyone building real-time AI inside editors, IDEs, or interactive tools.

I hope you find this interesting. Happy to answer any questions!

Re:Creating a Tab completion model from scratch on Wednesday November 19, 2025 @08:51PM (#45987860)
I’ve been experimenting with next-edit prediction for a while and wrote up how we trained the edit model that powers our Tab completion feature. This post is part of a broader series where we share how we built this feature from the low-level modeling right up to the editor extension.

The cool part is we fine-tuned Gemini Flash Lite with LoRA instead of an OSS model, helping us avoid all the infra overhead and giving us faster responses with lower compute cost.

Show HN: Run parallel agents in VSCode tabs

https://docs.getpochi.com/developer-updates/2025-11-11-launch-parallel-agent/

Though parallel agents already exists, we built this out differently in Pochi. Most other tools operate inside a single editor tab. That means there is at most one conversational state to interact with.Parallel Agents in Pochi use separate Git worktrees, so each task has its own working directory, branch, chat history, and terminal environment. Task state stays isolated.From a UX standpoint,...

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Re:Ask HN: What Are You Working On? (October 2025) on Sunday October 12, 2025 @05:19PM (#45562009)
I've spent the last few months working on a custom RL model for coding tasks. The biggest headache has been the lack of good tooling for tuning the autorater's prompt. (That's the judge that gives the training feedback.) The process is like any other quality-focused task—running batch rating jobs and doing SxS evaluations—but the tooling really falls short. I think I'll have to build my own tools once I wrap up the current project

Show HN: Turn Claude Code sessions into beautiful web links

https://github.com/wsxiaoys/claude-code-share

I got tired of screenshotting claude code sessions to share with teammates, so I built this. It converts session into beautiful web pages with proper rendering of tool calls.Works by parsing CC's session, extracting steps, then uploading to a simple sharing service. Takes about 2 seconds to generate a shareable link.Live examples:- https://cc.getpochi.com/s/IldMh27j (Research claude-code-share...

( Read More... | no comments | 5 points | on HN )
Re:Tabby: Self-hosted AI coding assistant on Monday January 13, 2025 @01:49AM (#42680750)
Appreciated! Fixed
Re:Tabby: Self-hosted AI coding assistant on Sunday January 12, 2025 @10:31PM (#42679801)
Yes - however, the FIM model requires careful configuration to properly set the prompt template.
Re:Tabby: Self-hosted AI coding assistant on Sunday January 12, 2025 @07:49PM (#42678743)
> So using 2 NVLinked GPU's with inference is not supported?

To make better use of multiple GPUs, we suggest employing a dedicated backend for serving the model. Please refer to https://tabby.tabbyml.com/docs/references/models-http-api/vl... for an example

Re:Tabby: Self-hosted AI coding assistant on Sunday January 12, 2025 @07:40PM (#42678671)
Tabby comes with builtin RAG support so you can add this api framework to it.

Example: https://demo.tabbyml.com/search/how-to-configure-sso-in-tabb...

Settings page: https://demo.tabbyml.com/settings/providers/doc

Re:Tabby: Self-hosted AI coding assistant on Sunday January 12, 2025 @06:00PM (#42677888)
Not yet, consider subscribe https://github.com/TabbyML/tabby/issues/322 for future updates!

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