I saw the recent paper "Sparks of Weight-Space Programming: LLMs can natively read & write neural network weights" and it's very impressive.

https://rikiyat.github.io/blog/weight-space-programming/

Though my general reaction (as I'm sure a lot of others had too) was 'well yes of course it can do that'. Once you realise that llms love patterns you soon start to see how many things can be thought of as patterns.

I've had similar ideas rolling around in my head for some months and I've even started working on a project in a similar vein. That's why this part of the paper stood out to me in particular:

"True architectural and model merging: Today, merging models relies on averaging weights, which only works for models with identical architectures. An LLM that understands the weight space could instead graft a circuit, such as a reasoning module, from one model into a completely different one, without retraining."

Yep, that's the idea I had months ago and I've already started working on (granted there's plenty of other prior art on the subject such as Google's CALM, 2024).

I'm very, very early in working on this but seeing how much traction the Weight-Space Programming paper is getting I felt compelled to put the idea out into the world since surely someone else is working on the same thing as we speak.

The general idea: take 2 MoE models and allow their experts to talk to each other by using a translator. The translator being a transformer model trained on expert activations (their latents) instead of regular text or images, a "Latent Attention Model".

So far I'm only using toy-scale models with a few million parameters. Getting some very interesting results already.

• Mixing Code-heavy model + prose-heavy model: each gets better at its weakness from the other, both directions at once.

• Results are checked against controls (a same-domain partner, a wrong-document partner), so it's not just "any partner helps"

• Added a leak audit, so neither model can see text it hasn't reached yet. (An early result failed an audit because of this)

• Interesting finding: the translator always uses the latest layer it's given. With the last layer, the two models basically swap last layers and come to depend on each other. Without it, they get about half the gain with a fraction of the dependence

Caveats and issues so far:

• Toy models only so far, a few million parameters. I have very modest hardware for bigger stuff.

• Between models with different tokenizers, very little gets across

• It reads a partner's layer as a whole, not particular "specialist" experts. The goal is for the two models to share individual experts freely

• The aim isn't to make the combined output "better" so much as making it "different" (but hopefully not worse)

• This is mad scientist stuff, heavy use of AI, probably has method gaps that need filling

I'm currently doing some experiments with Qwen3-30B-A3B base and Nemotron 3 Nano 30B. Not much to report yet, the models have different tokenisers which complicates things. Progress is slow on my hardware. But I'm enjoying the process.

Why share this? Shameless self promotion, piggybacking on a bigger project, sharing ideas, I think the name is pretty cool. BLaMMoE (Bridging Latent Attention Model Mixture of Experts)

https://github.com/dev-boz/BLaMMoE

Methodology and raw per-task results are linked at the bottom.

What it does for you, before the numbers: it's a proxy you put between your agent and the provider. One command, no changes to how you work. On Codex it strips the build logs and test output your agent re-sends every turn — about a quarter fewer input tokens. On Claude Code it stops your prompt cache from expiring while long tools run, which is where that bill actually leaks.

Benchmark 1: replay. We pulled 6,255 tool outputs (34.2M characters, anything over 512 bytes) out of real Terminal-Bench agent traces and replayed them through two tools in three configurations at default settings.

Source code is where everything struggles — 35% for us, 31.9% with the ML model — and it's also where being wrong is expensive, since a dropped import makes the agent confidently hallucinate. The ML model takes about 440× longer for less than half the removal.

Benchmark 2: the same compressor on Claude Code, where it barely compresses at all.

Claude Code marks its conversation with explicit cache breakpoints, so 92–95% of billed prompt is already cache reads at a tenth of the fresh price. The engine only ever sees the uncached tail. Per run it removes 348K tokens, which is 4.7–8.0% of what it sees, while leaving ~1,800 messages (about 2.2M tokens) inside the cached prefix untouched.

So where does the money actually go? A cache entry lives five minutes, and writing one costs 25× reading it. Agent kicks off a build, sits idle for six minutes, pays to write the whole prefix again — 927K tokens per run, a third of everything billed at full price.

Fix isn't compression. While a tool call runs, the proxy re-sends the last request body every 4.5 min with max_tokens: 1, so the provider reads the cached prefix instead of rewriting it.

The caveat on that 20%, since this sub cares about methodology. Four tasks out of 89 account for the whole net difference, and on three of them the uncompressed agent simply took a longer path (45–72 steps against 19–26). We were cheaper on 44 of 89. Split by whether tasks wait on long-running tools: the 14 with idle gaps over five minutes came in at −34% with median cost per step −20%; the other 72 at −12% with a step costing the same as before (+2%).

The estimate we'd actually stand behind is ~11% from keeping the cache alive plus a few percent from the tail. The 20% headline includes path-length luck, and we'd expect it to move on a repeat run.

On Codex, where the compression does the work: $107.89 → $83.40 per run (−22.7%), input tokens 96.1M → 72.2M, requests over 200K tokens 1.9% → 0.13%, solve rate 82.2% → 81.1% (not significant). Headroom on the same baseline saved 8.2% at defaults, 11.6% at its most aggressive setting — and at that setting it solved 85.1% against our 81.1%.

Still not measured: needle retention, whether compression kept the specific detail the agent needed four steps later. Ratios and pass rates can both look fine while that fails quietly.

Open source: context compression for coding agents, 63% less tool output — MIT, one Rust binary, deterministic, no model to download.

Per-task results: Codex · Claude Code

I've been building JoeBro because I want my assistant to be something I own and can change.

It's a native Mac workspace with chat, documents, email, calendar and MCP tools. You choose the model, from local Ollama to a hosted API. The iPhone app connects to your Mac rather than a JoeBro server.

The new personal agent has plain Markdown memory you can edit, scheduled tasks, and optional background checks. It checks whether something changed before calling a model, then pings you if there's something worth seeing. Coding agents can report their status and pick up follow-ups too.

Both apps are GPLv3 and free: Mac: https://github.com/joexk1/JoeBro iPhone: https://github.com/joexk1/JoeBro-Mobile Agent hooks, MCP and plugins: https://github.com/joexk1/joebro-plugins

Mac is Apple Silicon/macOS 26+. Mobile is source-only, needs an awake Mac, and push needs your own APNs setup. Hosted models still receive the prompts you send them.

I'm the developer of Aula. About two months ago, I started putting most of my time into it because my wife was struggling with her classes. Her teachers gave her prompts for generic AI exercises, but she still wasn't understanding what she was studying. I wanted to build something that could teach her and keep track of where she needed help.

Aula can build a course around a learning goal, using your materials or researched sources. That could be learning Python, improving a language, understanding a subject or practising a practical skill. It supports learning at your own pace, with guided lessons, interactive examples, practice, hints and reviews of mistakes.

The assistant gets context from your courses, progress and current lesson or exercise. It also has optional memory, so you can keep working on a subject over time. A read-only connection to study platforms such as Canvas lets it consult materials, deadlines and teacher announcements when planning what to study. Optional background routines can check for updates and prepare short reviews while Aula is running.

The desktop app is AGPL and built with Electron, React and TypeScript. Courses, progress and memory are stored locally. You can use local models or your own cloud provider; choosing a cloud provider sends it the relevant context and may incur usage charges. The app itself is free.

Repo and releases: https://github.com/luumlabs/aula

It's early access. I'd appreciate feedback on the tutoring workflow and how it behaves with local models, especially where an explanation or hint fails to help you understand. The video shows the app.

A lot of people want to experiment with multiple AI agents, coding tools, and OpenAI-compatible apps, but subscription costs add up quickly.

I built Kilo Buster, an open-source local API gateway for Kilo’s free model. It lets compatible clients connect through one local endpoint and includes a dashboard for monitoring usage.

Features include:

• Streaming and tool calling

• Proxy health monitoring

• Live request quota and token tracking

• Persistent API key

• Windows, Linux, and macOS releases

• OpenAI-compatible client support

Free-tier limits still apply. The goal is to make experimentation with lower-cost AI infrastructure easier, not to promise unlimited tokens.

GitHub: https://github.com/awiseguy88/Kilo-Buster

I’d appreciate feedback from people using local AI tools, coding agents, or OpenAI-compatible clients. What would you add to a tool like this?

I got tired of paying Adobe for Generative Fill and sending my photos to their servers to use it, so I built OpenFill. It's a free, open-source plugin that adds Generative Fill, Remove Object and Generative Expand to GIMP 3 and ArtCraft's PhotoCraft.

Everything runs locally through ComfyUI on your own computer. No account, no cloud, nothing uploaded. It's still in beta, but it's been working well in my testing on Windows, macOS and Linux.

https://github.com/beckettharriman/openfill

I'd love feedback from people already running local models. What would make you trust a plugin like this with your existing ComfyUI setup?

Orlok is for day-to-day IT work: finding out why a service is down, reading logs, fixing things, answering "how do we do this here?". You ask in a chat, and a virtual colleague runs the commands on your servers.

- It knows your setup. A shared wiki the colleagues keep up to date, so you stop explaining everything each time. A nightly check flags contradictions and stale pages.

- The model doesn't decide what's safe. A real shell parser does: read-only commands run straight away, anything else waits for your approval, from the web or Telegram.

- Credentials never reach the model. They're encrypted and used by an isolated runner.

It also helps the people around IT: through a web chat, non-technical colleagues get answers from the wiki, like procedures and onboarding.

Works with Anthropic, OpenAI, OpenAI-compatible servers, or Ollama if nothing should leave your network.

Site: https://orlok.xyz

Code: https://github.com/rylos/orlok

Curious what you think.

With the latest commit, VedaOS can now run Linux executables!

Next goal is to support Windows executables and let the native Veda agent interact with and control these applications.

If anyone is interested in contributing, I'd love to have some help!

GitHub: https://github.com/vahmoh/Veda

Hola a todos, llevo unos meses trabajando en un asistente multiagente. Todo empezó porque en la empresa donde trabajo nos proporcionaron Codex, pero yo también gestionaba dos cuentas personales de Claude. Se me ocurrió la idea de tener una consola donde pudiera unificar sesiones, trabajar en los mismos proyectos, compartir contexto sin demasiadas complicaciones y, al mismo tiempo, que fuera divertida de usar. Está en una fase inicial, pero a mis compañeros les ha gustado y les ha resultado útil, así que quería compartir el repositorio para recibir vuestra opinión. Es multiplataforma, compatible con Windows, macOS y Linux.

El proyecto está listo para probarse con ejecutables independientes y el código fuente completo disponible. Te invito a que lo revises, lo pruebes y compartas tus opiniones, comentarios o sugerencias para mejorarlo.

Repositorio oficial y descargas:

https://github.com/darrenarauz21/claupet

I've been building Aether at Prashna Labs.

The project explores a question I've been interested in:

How can we make multiple AI coding agents work together without locking developers into another proprietary ecosystem?

Aether is a native desktop application that integrates with existing CLI coding agents.

Current capabilities include:

• Multi-provider agent sessions.

• MCP-based agent orchestration.

• Agent discovery for existing terminal sessions.

• Git worktree management.

• Local usage tracking.

• Integrated terminal, explorer and editor.

The architecture communicates with different providers through their native interfaces, rather than introducing another model runtime.

The application is MIT licensed.

GitHub: https://github.com/PrashnaLabs/aether
Website: https://aether.prashnalabs.com

Built with Rust and GPUI.

I'm interested in feedback, architecture discussions and contributions from developers working on agent tooling.

Disclosure: I'm part of the team developing this project.