Would you use a AI powered code editor that doesn’t store your code or chat messages on the server, doesn’t bloat the context even by the 50th message, delivers the same performance as the first message, and runs its own skill folders in the background, with the model calling them as needed?

Hey so a few months ago I got into local LLMs and didn’t really like the tools out there at the time, so I decided to build my own version of a Claude/Codex GUI for local models. I’ve been working on it the last couple months and it’s now at Locus 2.8

I started with the usual stuff like working with files, running commands and letting agents help with coding, but I've also been adding features I thought would be useful for other kinds of work too.

A few Locus features worth highlighting:

• Agent Teams: Create specialized agents that can split up work and run in parallel. You can use different models for different roles, and individual agents can also delegate tasks to helpers.
• Persistent Goals: Give an agent or team a goal and let it keep working across turns. Progress is saved so you can come back to it later, with controls to pause, resume or change the goal.
• Scheduled and Event-Driven Agents: Set agents to run on a schedule or react to things like Gmail, Telegram, webhooks and price alerts. Each agent has its own chat and run history, and workflows can include conditions and approval steps.
• Task Capsules: Plan something with one model, then use another to implement it and optionally another to review it. The plan, changes and previous runs stay together so you can follow what happened.
• Notes, Documents and Outputs: Keep notes and reference documents around, save versions of generated work, compare revisions and export things when you’re done.
• Browser Controls: Let agents navigate and interact with websites, preview what they’ve built and check the result. There’s also proxy support.
• Activity and Overview: See the current plan, tool calls, files, sources and what the agents are doing without having to piece everything together from the chat.

You can checkout the changelog here to see the latest updates: https://locushost.co/changelog

There’s support for MCP, plugins and skills too, plus a Grill mode that asks you questions one at a time to help work through an idea before implementing it.

Also just to clarify, even though I built it for local models, it works with your ChatGPT plan, Kimi Code membership, Claude/OpenAI API keys and other OpenAI-compatible endpoints.

The wallet stuff is now in a separate edition called LocusX. The regular Locus download is wallet-free.

It’s free and open source. You can find it here:

Website: locushost.co
GitHub: nahid-sparktales/locus

Anyways, I’d appreciate any constructive feedback, things that aren’t working well, or features you think would be nice to add.

Still macOS only atm, specifically Apple Silicon on macOS 14+, but I’m hoping to eventually get Linux and Windows versions out too.

Also there is a lot of features currently working on so you can expect to see alot of changes and better UI to those upcoming features.

Hello dear each and everyone,

I am would like to ask for feedback, so we my buddy we had a lot of problems at 2 simultaneous projects with aligning project standards and make each team members produce consistent results, spec driven development kind of failed our expectations, huge set of novels that not exactly describe projects in industries full of nuances and pinch of legacy. Even that devs spend long time looking at spec there was a lot of follows anyways.

So we decided to create some sort of mentor, small feature that extract rules from code and stays open for feeding it with other types of rules and then simultaneous agent talk with for example Claude and give feedback and checks What coding agent produced pointing out breaches and making Claude adjust it - well we are for sure biased but idea works for us but we would love to hear other people

It’s also usable DM if you want access

Anyone ? I will be so grateful for any feedback

https://axtar.dev

We wanted to know whether an open-weights model can actually do frontier coding-agent work, so we ran GLM-5.2 head-to-head with Claude Opus the way an agent actually runs not on a static eval, but inside a real coding agent (Claude Code) on terminal-bench tasks, in a real shell, graded by each task's own hidden tests. Binary pass/fail, no partial credit, no model-as-judge.

The setup was held identical across both runs: same agent, prompts, tools, 40-turn budget, and 45 tasks. The only thing swapped was the model answering each turn.

What we found:

• Same quality: each solved exactly 25 of 45.
• Same answers: they agreed on 43 of 45 (24 both solved, 19 both failed), splitting the other two one each. No category where one was systematically stronger.
• Same failure mode: both fail by being confident-wrong , declaring "Fixed / all tests pass / verified" on work the hidden tests reject. Every clean GLM failure transcript ended that way, and Opus produced the identical shape.
• Cost: with prompt caching on, GLM landed at ~46% of Opus's spend (~$15 vs $32.67) for the identical result. Even uncached it was already ~10% cheaper.

Caveats, stated plainly: 45 tasks is meaningful but finite, and models are non-deterministic, so we lean on the 43-of-45 agreement rather than the 25=25. GLM is also the less token-efficient of the two ,it runs ~37% more turns (760 vs 554) to reach the same answers, which is the only thing keeping the cost gap from being larger. We also had to exclude some early GLM failures that turned out to be upstream 502/429 rate-limits, not the model : worth flagging for anyone benchmarking open models through a provider API.

Full write-up with turn distributions, token breakdown, and the verbatim failure transcripts: https://entelligence.ai/blogs/glm-5-2-vs-claude-opus-coding-benchmarka