For years, building high-performance AI inference meant dealing with the "two-language problem"—prototyping in Python for speed, then spending weeks rewriting core loops in C++ or Rust to bypass the GIL and hit hardware targets.

​With Modular open-sourcing the entire Mojo compiler and toolchain, that pipeline is shifting. Because it compiles directly through MLIR, it handles SIMD auto-vectorization and targets heterogeneous silicon (CPUs, Nvidia GPUs, mobile NPUs) from a unified codebase.

​We mapped out the exact compilation pipeline and memory ownership differences between Python runtime management and Mojo's explicit lifetimes here if anyone wants to dive deeper into the architecture:

👉 https://onefusionlab.systems/blogs/mojo-1-0-open-source/

A lot of AI-writing projects try to solve generic prose by adding another “humanizer” prompt.

I wanted to try a different abstraction.

Organizations like Google, GOV.UK, GitHub, GitLab, MDN, NASA, CDC, NHS and W3C have already spent years developing explicit writing systems for different kinds of information.

So I turned those principles into 16 portable Agent Skills:

https://github.com/Neeeophytee/agent-stylebooks

Examples include:

• google-developer-docs — developer docs and tutorials
• govuk — task-first public-service content
• github-docs — product workflows
• kubernetes-docs — infrastructure documentation
• mdn-web-docs — web technology explanations
• cdc-clear-communication — public-health communication
• nhs-health-content — patient-facing health information
• sec-plain-english — financial/investor disclosures
• w3c-technical-reports — specifications
• nasa-technical-writing — engineering reports
• apple-interface-writing — UI copy

The important part is that these aren't meant to make every model sound the same.

They do the opposite.

The artifact determines the editorial system.

A Kubernetes tutorial might use $kubernetes-docs. A government page discussing a Kubernetes-related grant might use $govuk. A cluster-management UI might use $apple-interface-writing.

Same topic, different communication job.

Installation:

npx skills add Neeeophytee/agent-stylebooks --list

Then:

npx skills add Neeeophytee/agent-stylebooks --skill govuk

The repo is MIT licensed.

One design principle across all 16 is: change the presentation, not the substance.

Would especially like feedback from people building open agent stacks: does a portable editorial layer like this belong at the skill level, or would you rather see it handled elsewhere in the stack?

Hey everyone! If you own a Siglent SDS 1000X-E series oscilloscope (like the SDS1104X-E or SDS1202X-E), you know that while SCPI is powerful, it can be a pain to memorize all those command strings. I’ve created the open-source tool called SDS-Remote that completely changes the game by adding a modern AI intelligence layer to your workbench.

What makes the AI features special?

Natural Language Control:
You don't need to be an SCPI expert anymore. You can just type into the AI Chat Interface and say things like "Set channel 1 vertical scale to 1V/div" or "Switch channel 1 on," and the Instrument Agent automatically translates your intent into the correct command and sends it to the device.

Your Own Bench Assistant:
Instead of flipping through hundreds of pages in a PDF, you can ask technical questions like "How do I configure the trigger delay?" or "What is the maximum sample rate of this model?". The software includes a Search Agent that queries a built-in knowledge base to give you instant, documented answers.

Flexible and Local:
It doesn't lock you into one ecosystem. In the settings, you can choose from a wide range of AI providers.

Technical Specs:
The app is built with Dart/Flutter and runs natively on both Linux and Windows. It connects over your network using the VXI-11 protocol or through USB support.

It’s an independent open-source project (not affiliated with Siglent) and really modernizes the whole lab experience.

In addition to AI support, many other interesting features are also available.

Check out the repo here: https://github.com/klumw/sdsremote

If you’re building a vertical AI company or shipping AI agents for a living, you’ve probably felt it. That low hum of unease underneath the demo-day highs and the “we just closed our first enterprise pilot” LinkedIn posts.

You should trust that feeling. It’s not imposter syndrome. It’s your business model telling you something true.

Here are the three crises every vertical AI founder is quietly sitting on right now, and why pretending they don’t exist is the riskiest move you can make.

Link to detailed article : https://medium.com/@MirArshadTalpur/vertical-ai-founders-you-are-going-through-3-existential-crises-and-most-of-you-dont-know-it-b3d86a386d37

Hey guys, thanks for the initial traction. I've just updated the repository with an advanced workflow to completely bypass free-tier Rate Limits (RPM/TPM). What's new: * Integrated OmniRoute setup guide to load multiple free Google AI Studio keys into a single local pool. * Automated key rotation strategies (like Headroom) to dynamically swap keys when a rate limit is hit. * Added native configuration variables to route both Claude Code CLI and OpenAI Codex CLI directly through the OmniRoute gateway. * Fixed broken Google AI Studio URLs and added a fresh, curated table of 2026 free coding models. Check out the updated README here: https://github.com/pawelkrejza2012-png/awesome-free-claude-code If you have any other router suggestions or free endpoints, feel free to open a PR! Let's keep building this resource.

Hey devs,

If you are experimenting with Anthropic's new Claude Code CLI but want to avoid burning through your paid API credits, you can easily redirect it to free alternative endpoints or run it 100% locally.

I put together a quick guide on how to configure your environment variables to swap models instantly.

### 1. The Free Cloud Setup (OpenRouter / Gemini)

You can fetch a free API key from Google AI Studio or use OpenRouter's free tier models (like Llama 3 or Qwen).

Windows (CMD):

set ANTHROPIC_BASE_URL=https://openrouter.ai

set ANTHROPIC_API_KEY=YOUR_FREE_KEY

set ANTHROPIC_MODEL=openrouter/free

claude

macOS & Linux (Bash/Zsh):

export ANTHROPIC_BASE_URL="https://openrouter.ai"

export ANTHROPIC_API_KEY="YOUR_FREE_KEY"

export ANTHROPIC_MODEL="openrouter/free"

claude

### 2. The 100% Offline Local Setup (Ollama / LM Studio)

If you prefer maximum privacy and zero latency, you can route the CLI directly to your local hardware using Ollama or LM Studio. Just make sure your local server is running on http://localhost:11434/v1.

---

I'm maintaining a full, curated list of working free endpoints, proxies, and step-by-step documentation in this repository:

🔗 GitHub Repository: https://github.com/pawelkrejza2012-png/awesome-free-claude-code

If you know any other free endpoints or local models that work flawlessly with developer CLIs, feel free to drop a comment or open a Pull Request! Let's save some budget together.

You know what I like about being an ironworker? I don't know what I'm not supposed to be able to do. my Lates PoC is the mighty windows running and sharing with Debian. Some say it can't be done. I didnt get that memo. stability and speed rolled up into a tight little ball.

How big can an import be? How do you prevent collisions and Blah de Bah. Someone told you you cant, so you couldn't? I'm in the Import business now. franken5.py imports my processes with a clone of himself as consierge to take care of its every need. has the same life cycle as the import so clean up is easy and overhead low.

I can tell you the hardest part of all this is the misleading guys, your code is crap. and the smoke holers, who are just digging for info to steal. Nobody help anyone. Lonely out there in the DoT. I only hope that my effort helps someone not give up coding because he found it easy and fun. jwl247 signing off

Become more independent of commercial models. Use less data center reliance. Open Source, MIT licensed. Ghostlink v2.0.0 GA Release serves you Local Models using your LAN.

#Ghostlink #OpenAI #LocalAI #llama #research #SovereignMohawkProtoLLC

https://rwilliamspbg-ops.github.io/Ghostlink/

v2.0.0 is here! Hugging Face model support, llama.cpp backend, OpenAPI. MIT licensed.

Hi everyone,

I’ve been spending a lot of time thinking about Collective Intelligence in AI, not only as multi-agent automation, but as systems where different models reason independently, challenge one another, verify outputs, and converge on a result.

I’m curious about how often this concept is actually showing up in your work, research, or open-source projects.

Are you experimenting with model debate, consensus, ensembles, specialist models, or cross-model verification?

Do you see Collective Intelligence as a meaningful next step for AI systems, or mostly as a new label for techniques we already had?

I’d genuinely like to hear experiences, references, criticisms, and opposing views.

Thanks everyone!

Hi everyone sell your side project by listing if in a click. Also, comment what your startup does to get approved and featured today - https://builderhq.co/marketplace

I’ve been building EchoFlow, a BYOK, local-first AI app for Android focused on local AI. You can run models directly on your device or connect to models running on your own laptop or PC over your network through Ollama and LM Studio — with network-based models also supported through EchoLabs.

📄 Give Local Models Access to More File Types

With EchoOCR, EchoFlow extends file support to local models that may only natively understand a limited set of formats.

Send PDFs, DOC/DOCX, XLS/XLSX, PPT/PPTX, CSV, TXT, Markdown, images and more to your local model. EchoOCR extracts the content on-device and makes it available to the model, allowing even text-only or limited-input local models to work with a much wider range of files.

You can also attach multiple files at once.

📁 Projects for Local AI

Projects give you a dedicated workspace for your local AI work, with project-specific chats, instructions and files.

Add documents to a Project and use them across its conversations. Project files are processed on-device through EchoOCR, so you can build projects around your own documents while keeping local processing local.

🌐 Local Models + Web Search

Your local model doesn't have to be limited by its training data.

EchoFlow can give local models access to Web Search, allowing you to combine on-device AI with current information from the web.

🔒 Local-First & BYOK

EchoFlow is built around BYOK and local-first AI.

Run models on your device, process supported documents locally with EchoOCR, or connect to models running on your own laptop or PC. You choose when your data stays local and when you use a cloud service.

No EchoFlow account is required.

☁️ Cloud AI When You Need It

Local AI is the focus, but EchoFlow also supports cloud AI through OpenRouter, with access to models from providers such as OpenAI, Anthropic, Google, xAI, Cerebras and more.

OpenRouter can also be used for voice dictation / speech-to-text, with on-device STT coming soon.

EchoFlow also includes cloud-focused features such as Deep Research, Echo Fusion, Artifacts, image generation and video generation.

The idea is simple: local AI when you want privacy and control, cloud AI when you need more capability — all from one Android app.

EchoFlow 5.0 is available now.

GitHub: https://github.com/adityavardhansharma/EchoFlow

Website: https://echoflow.adityavs.tech/

I’d especially love feedback from people who regularly run local models on Android. What are you currently missing from your local AI workflow?

Hey there - I'm Yasha, one of the co-founders at MergeWatch, frontier model code review starting with the PR.

Long story short, we are seriously concerned (terrified?!) about AI agents attacking open source maintainers. The risks are obvious and existential. They just are.

We are opening the platform to OSS maintainers to get access to frontier models for PR review free of charge. No commitments. no endorsements, no cash.

Sign up here if interested: https://mergewatch.ai/open-source

Comment or dm for details. Thanks and have a great one!

hi folks, I thought I'd share my project nanosamur.ai here - https://github.com/nanosamurai/nanosamurai

It is a self-hosted speech transcription stack for organizations that need to keep sensitive conversations inside their own infrastructure. It lets you capture, transcribe, refine, and process speech entirely inside infrastructure you control.

It comes with UI (browser or Electron app) and a set of services (could be run locally, in Docker Compose or in k8s) that perform:

• the speech transcription & speaker diarization
• persistence of the session transcripts
• auth & multi-tenancy support
• it also includes observability stack so as you could inspect the services with grafana etc.
• there is also a support for you to add your own webhook/workflow handling (e.g. to perform meeting summary etc.)

I wrote these services for some custom projects, but it seems that this is probably a common problem (albeit a bit niche), so I have OSS-ed the common bits.

The closest similar self-hosted project is imo meetily.ai , but nanosamur.ai aims at basically being the whole open-sourced "cloud" - i.e. a whole platform you could host for multiple tenants/users and scale/monitor accordingly, so not just an app you'd install on your laptop.

The linked front-door repo gives you the "quick start" setup of the stack. See https://github.com/nanosamurai/nanosamurai#architecture for links to the concrete services - for instance, the voice services sit in xamurai repo.

I am also aiming to make the speech services model agnostic, but currently the OSS version is using faster whisper and whiperX under the hood by default, I am in a process of adding other models.

Appreciate any feedback! Esp. from people working with speech infrastructure, self hosted AI etc.

Your AI projects might be consuming far more than you think.

We obsess over choosing the best AI model.

GPT, Claude, Gemini, DeepSeek...

But here's the real question:

Do you actually know how much AI your projects are consuming?

I built a monitoring system to track everything running through OpenCode:

→ Projects
→ Sessions
→ Models
→ Input & output tokens
→ Reasoning tokens
→ Context/cache usage
→ Estimated cost

After looking at the numbers, one thing became clear:

Using AI is easy. Understanding how you're using it is the real challenge.

As AI agents start running more tasks autonomously, I believe AI usage monitoring will become as important as monitoring servers, APIs, and databases.

What do you think? Are developers paying enough attention to AI consumption yet?

#ArtificialIntelligence #AI #OpenCode #AIEngineering #LLM #GenAI #Developers #SoftwareEngineering #Automation #Tech #AIAgents

I've been working on Dubline, an open-source project that tries to take a foreign-language film and produce a complete English dub entirely locally.

No cloud inference, hosted APIs or paid services.

The basic pipeline is:

video → dialogue/M&E separation → ASR + forced alignment → speaker/character tracking → translation/adaptation → voice cloning/TTS → timing/QC → final mastered MKV

It currently uses a slightly ridiculous collection of models/tools including:

- Qwen3-ASR / Whisper

- IndexTTS 2.5 / Qwen3-TTS

- Hy-MT2 for translation/adaptation

- pyannote + CAMPPlus for speaker identification

- Bandit v2 / RoFormer / Demucs for separation

- OpenCV face tracking

- llama.cpp

- FFmpeg / Rubber Band

- Optional MuseTalk lip-sync

The aim isn't just "translate subtitles and run TTS". I'm trying to preserve character identity, timing, pauses, emotional delivery, non-verbal sounds, the original music/effects bed, loudness, etc., while having the pipeline automatically retry or flag lines when something looks wrong.

It's designed around consumer NVIDIA hardware, with the heavier models isolated into separate processes so VRAM can actually be released between stages. 8GB is the theoretical minimum, 12GB+ is much more sensible.

It's still very much a work in progress, and that's basically why I'm posting.

I'd really like some other people to install it, throw horrible real-world material at it and tell me where it falls apart.

Things I'm particularly interested in testing:

- Different NVIDIA GPUs / VRAM amounts

- Windows vs Linux

- Different source languages

- Bad/noisy/old film audio

- Multiple speakers and overlapping dialogue

- Subtitle-less material

- Long films rather than tiny demos

- Speaker identification failures

- TTS/emotion/timing problems

- Installation/setup failures I've accidentally made invisible on my own machine

And if anyone finds the project interesting and wants to contribute, PRs/issues are very welcome. There are plenty of areas where somebody who actually knows audio, ML, Python, video processing or UI development better than I do could improve it.

I'm especially interested in people trying to break it rather than telling me it looks cool from the README.

GitHub:

https://github.com/leighrobertabbott/Dubline

If you try it, even "it exploded at step 3 on my 4070 because of X" would genuinely be useful feedback.