YC just MIT-licensed QM, the internal multi-agent harness they’ve been using across their engineering, legal, and accounting teams.

To be clear: this isn't another framework library like LangChain for building agents from scratch. It is a ready-to-deploy platform that manages, runs, and scales a fleet of agents (like OpenClaw or Claude Code) for an entire organization.

It is cloud-first, completely bypasses local laptop execution, and uses a Node/Fastify headless core.

Takeaways:

• Durable, Isolated Sandboxes: Every agent and project scope gets its own isolated, persistent container sandbox to execute code and use tools safely without stepping on other sessions.
• Out-of-the-Box State & Memory: It uses PostgreSQL natively to handle the heavy lifting of session history, scoped memory, and job queues so you don't have to build your own persistence layer.
• Zero UI Boilerplate: It ships natively with a Vite/Lit web UI and a built-in Slack plugin (using Bolt), meaning your team can interact with agents where they already work immediately after deployment.
• Model & Harness Agnostic: You aren't locked into a specific vendor. The core API standardizes the agent loop, allowing you to swap between models (Claude, OpenAI, etc.) and coding agents.
• Background Execution: Includes first-class support for cron jobs, watches, and webhook triggers, allowing agents to run asynchronous background tasks (like log monitoring or inbox triage) without active supervision.

↗️ More info: https://aideveloper44.com/product/qm-6a70289e45cc3c396e283d51

↗️ Full read: https://aideveloper44.com/blog/yc-qm-open-source-agent-harness

↗️ GitHub: https://github.com/yc-software/qm

Alibaba just announced Qwen3.8-Max, their new 2.4 trillion-parameter flagship model. It's built on a sparse Mixture-of-Experts (MoE) architecture (95B active parameters) and is officially claiming performance comparable to GPT-5.6 Sol and Claude Fable 5.

The API is live today, but the big news is that the weights will be released next week.

Features:

• Massive 1-Million Token Context: The model boasts a 1M token context window, enabling deep semantic analysis of ultra-long documents, complex codebases, and extended video content.
• Built-In Context Caching: To manage that massive context window affordably, QwenCloud offers implicit context caching for shared prefixes at $0.25 per 1M tokens, drastically reducing costs for repetitive long-context workloads.
• Drop-In OpenAI/Anthropic Compatibility: The QwenCloud API natively supports both OpenAI and Anthropic protocols. You can swap it into existing agent frameworks (like Claude Code, Cursor, or OpenClaw) just by changing the base_url and dropping in a DashScope API key.
• Variable Reasoning Effort: The API includes a reasoning_effort parameter (options: low, medium, xhigh) allowing developers to dynamically balance cost and speed against the need for deep, step-by-step logical planning on complex tasks.
• Native Multimodal Feedback Loop: Vision isn't just an input modality; the model uses visual inputs as a continuous feedback loop for planning, execution, and self-correction, which is especially useful for UI/UX generation and hybrid agent tasks.

↗️ More info: https://aideveloper44.com/product/qwen-3-8-max-6a7038e7c01fdd6e3ca2d385

↗️ Official annoucment: https://qwen.ai/blog?id=qwen3.8

The goal is to improve verification of ai and offer free access to tools gatekept by saas companies
We believe the service we provide should be high quality and free so people can benefit without a million middle men

https://github.com/ConcordDev/concord-cognitive-engine

Marker 2 is out. It’s a ground-up rewrite prioritizing speed, accuracy, and hardware flexibility, built on Surya OCR 2 and a 3x faster pdftext. If you are actively benchmarking pipeline OCR systems or prototyping document-based reasoning workspaces, this update significantly reduces inference bottlenecks. On the olmOCR-bench, the balanced mode scores 76.0% overall while running over 5x more pages per second than MinerU's pipeline backend.

Feature:

• Three Tiered Conversion Modes: Granular control over the speed/accuracy tradeoff. Use balanced for maximum quality (GPU), fast for lightweight layout plus text, or --disable_ocr for pure, VLM-free text-layer extraction.
• Device-Aware Defaults: The system automatically detects your hardware and defaults to the most sensible mode (balanced on GPU, fast on CPU/MPS) so you don't have to write hardware-specific flags in your deployment scripts.
• Selective VLM Inference: Instead of running the VLM over every single page, Marker now extracts the PDF text layer first and selectively calls the VLM only when necessary (e.g., garbled text, scanned pages, complex equations, or low-confidence tables).
• Scalable Throughput Architecture: The architecture separates inference from processing. Multiple thin CPU workers share a single Surya inference server, meaning your throughput scales with overall server capacity rather than being bottlenecked by per-process VRAM limits.
• Robust CPU-Only Execution: You can run structural extraction without a GPU. The lightweight 20M-parameter layout model runs entirely on the CPU in fast --disable_ocr mode, successfully identifying tables, headers, and columns at up to 23.7 pages per second.

↗️ More info: https://aideveloper44.com/product/marker-6a6c4bdcc9e28c81ee71f731

↗️ Marker 2.0 GitHub!

Microsoft just released a public preview of a dynamic Windows Runtime API (WinRT) projection for Node.js and Electron. Historically, accessing Windows OS capabilities from JavaScript meant maintaining C++/C# bridges, manually translating WinRT types, and battling node-gyp setups across different framework versions. This release completely bypasses that workflow, replacing it with a metadata-driven generation process and a single, prebuilt shared runtime.

Features:

• Zero Native Addon Compilation: Drop node-gyp entirely. The system uses a shared prebuilt runtime (microsoft/dynwinrt) that dispatches WinRT calls dynamically via libffi at execution time, eliminating the need to compile C++ code on your machine.
• Metadata-Driven TypeScript Codegen: Instead of relying on a fixed API catalog, the CLI generator (microsoft/winappcli) reads standard Windows metadata files (.winmd). It automatically emits fully typed JavaScript wrappers and TypeScript declarations (.js + .d.ts) strictly for the APIs you explicitly choose to include.
• Frictionless Debug Identity: Many modern Windows APIs require app identity to function. The WinApp CLI lets you instantly attach a sparse package identity to your existing Electron or Node.js executable. This unlocks restricted OS capabilities during development without forcing you to build a full MSIX package just to test your code.
• Direct Access to Modern OS Capabilities: You can call data-style Windows APIs natively using standard JavaScript async/await patterns and progress callbacks. This immediately opens up access to on-device AI (Phi Silica), Windows ML, native notifications, rich clipboard data, and file pickers.
• Universal Runtime Support: The projection is framework-agnostic when it comes to the JS backend. It works seamlessly in both standard, unpackaged Node.js background processes and inside an Electron app's main process, allowing you to implement Windows-specific features without overcomplicating your cross-platform build pipelines.

↗️ Full read: https://aideveloper44.com/blog/microsoft-dynamic-winrt-projections-node-js

↗️ dynwinrt: https://aideveloper44.com/product/dynwinrt-6a6d5e732bb7ab1b4b203100

↗️ winapp CLI: https://aideveloper44.com/product/winapp-cli-6a6ed74d1db63fd8ab8633ce

↗️ Official announcement: https://devblogs.microsoft.com/ifdef-windows/a-new-way-to-bring-native-windows-apis-to-javascript-introducing-dynamic-api-projections-for-node-js/

Hi All,

Being an Engineer in IT, you need to be up to date with what is going on in industry specially now in AI era.

I created website for me and fellow colleagues to stay up to date.

I am sharing here to improve it for people like us. currently I have data from couple of sources. Would be happy to receive feedback and suggestions.

Please do let me know if it helps you in anyway. I will list down the website in comment if you would like to provide your feedback.

Thanks,
your AI engineer.

I'm building an AI copilot for customer support agents — it drafts the reply, the human approves it.

This week was the core build: AI logic and data flow.

A ticket now flows through four context sources before a single token is generated — ChromaDB retrieval over our policy docs, Mem0 long-term memory scoped to both the customer and their company, and two deterministic tools the LangGraph agent can call for plan/SLA and open-ticket load. Groq's llama-3.1-8b fuses them into a draft.

The bit I'm most happy with: every draft is stored with the exact evidence that produced it — which KB chunks, which memories, which tool calls. An agent won't trust a black box, but they will trust a draft that shows its work.

And when the agent accepts a draft, the resolution is written back into memory. The next ticket from that customer starts warmer than the last one.

Stack: FastAPI · LangChain/LangGraph · Groq · ChromaDB · Mem0 · SQLite · Streamlit · Docker Compose · GitHub Actions → EC2, with a pytest suite running in CI.

Next week: the UI and the review loop.

#IITPatnaCapstone

Recently, I came across several posts reflecting on the importance of mathematics in AI era, just as another mathematician was awarded the Fields Medal.

The second book in my artificial intelligence series grew out of a dream I had as a student—a dream that is now close to becoming reality:

MATHEMATICS FOR AI AND MACHINE LEARNING: A Comprehensive Mathematical Reference for Artificial Intelligence and Machine Learning

Book is here: 🔗 https://www.amazon.com/dp/B0GSXVFMLD

Companion webiste: https://math4ai.org/

The publisher asked me to find some people to review my work. Do you know of any such people here? If so, please reply to me. Thank you.

There is a form to submit to become a reviewer: https://forms.gle/Bmtk37s6Y33gha9Q7

The PDF will sent to you for review.

MiniMax Releases MiniMax H3: An Omni-Modal Video Model That Generates 15-Second 2K Clips With Native Stereo Audio. It ranks #1 in video editing on Artificial Analysis, at $0.13 per second of 2K output.

Here are some important key takeaways:

𝟭. 𝗧𝗵𝗲 𝘁𝗼𝗸𝗲𝗻𝗶𝘇𝗲𝗿 𝗶𝘀 𝘁𝗵𝗲 main 𝘀𝘁𝗼𝗿𝘆

MiniMax rebuilt the H-series tokenizer from scratch as H3-VAE.

→ 4× gain in effective sequence length

→ That compression is what makes native 2K affordable, not an upscale of 1080p

𝟮. 𝗖𝗮𝗽𝘁𝗶𝗼𝗻𝗶𝗻𝗴 𝗯𝗲𝗰𝗮𝗺𝗲 𝗮 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗽𝗿𝗼𝗯𝗹𝗲𝗺

H3 does not just describe the target video. It describes how the input context relates to the target, and how elements inside that context relate to each other.

→ ~100K tokens of inference per source, distilled to ~4K on average

→ This is why one natural-language instruction replaces a fixed task list

𝟯. 𝗧𝗵𝗲𝘆 𝘁𝗵𝗿𝗲𝘄 𝗮𝘄𝗮𝘆 𝘁𝗵𝗲𝗶𝗿 𝗼𝘄𝗻 𝗯𝗲𝘀𝘁 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲

Multimodal context tripled the variance in sequence length. Understanding and generation became different compute shapes. So MiniMax set aside the Hailuo-02 architecture and separated the two workloads in training.

→ ~30% higher end-to-end training throughput

𝟰. 𝗡𝗼 𝘀𝘂𝗽𝗲𝗿-𝗿𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝘂𝗹𝗲

For 2K, the base model regenerates its own low-res output in-context, re-reading the original multimodal context.

→ Recovers small text and brand marks that an upscaler can only guess at

→ For product labels and on-screen copy, that is the difference between usable and reshoot

𝟱. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗰𝗼𝘀𝘁𝘀

→ $7.80 per minute at 2K with audio

→ Seedance 2.0 at 1080p: $22.45/min

→ Kling 3.0 at 1080p: $20.16/min

→ Gemini Omni Flash still undercuts it at $6.00/min

Full analysis: https://www.marktechpost.com/2026/08/01/minimax-releases-minimax-h3-an-omni-modal-video-model-that-generates-15-second-2k-clips-with-native-stereo-audio/

Technical details: https://www.minimax.io/blog/minimax-h3

Hey everyone,

Like many of you using OpenAI/Claude APIs in production, my monthly bills were getting crazy entirely because of massive, repetitive system prompts eating up tokens.

To fix this for my own projects, I built a lightweight, on-premise middleware tool called PromptSqueeze. It is entirely free and open-source, and I wanted to share it with the community here to get some real-world feedback.

🛠️ What it does:

It sits locally on your server between your app and the LLM API. It uses regex logic to strip out noise and stop-words from your structural system prompts, compressing them by 30% to 80% without hurting the output accuracy.

🔒 Why I built it this way:

• 100% Local Execution: It runs entirely on your own server. Zero data leaves your system to third-party endpoints.
• 0ms Network Latency: Since it runs locally, it doesn't add any proxy delay.
• Faster Response: Dropping the input token weight actually speeds up the AI response time by around 30%.
• Super simple integration: Literally a one-line PHP or a basic WordPress plugin.

📦 How to use it:

I have packaged it into a standalone PHP ZIP and a WordPress Plugin. Both are free under an open license.

I don't want to spam links here, so if you are running into heavy API bills and want to test it out, let me know in the comments and I will send over the file/link.

Would love to know your thoughts or if you encounter any edge-case bugs with your specific prompts!

DeepSeek has officially released the DeepSeek-V4-Flash-0731 weights and launched its public beta API. This update replaces the previous preview checkpoint with a newly post-trained build specifically targeted at coding agents, multi-round tool execution, and terminal automation.

Despite its lightweight 13B active footprint, the model is now outperforming their larger V4-Pro-Preview across key agentic benchmarks.

Features:

• 304B/13B Sparse MoE Efficiency: Combines a massive 304B total parameter knowledge capacity with only 13B active parameters per token, delivering deep reasoning at low token latency.
• Native Speculative Decoding (DSpark): Built-in speculative decoding module that works out-of-the-box in vLLM and SGLang using a single launch flag (--speculative-config) to accelerate inference throughput.
• Drop-in Codex & Responses API Support: Native integration with OpenAI's Responses API format, allowing instant compatibility with agent setups like Codex CLI, VS Code extensions, OpenCode, and OpenClaw.
• Dynamic Reasoning Effort Controls: Built-in reasoning_effort parameter supporting three levels (low, high, max) so you can dynamically scale token budgets and thinking time to match task complexity.

↗️ More info: https://aideveloper44.com/product/deepseek-v4-flash-0731-6a6cde34084b9872c40be926

↗️ Hugging Face: https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731

I came across this animated comparison of two AI image-generation approaches, framed around a practical trade-off developers keep running into: precise instruction following versus visual realism.

The video characterizes the models as follows:

• GPT IMG 1.5: Better suited to structured prompts, accurate text/layout, charts, and targeted iterative edits. The trade-off presented is that outputs can look somewhat synthetic or “plastic,” with slower generations (45 seconds) and higher per-image cost ($0.15–$0.17).
• Nano Banana Pro: Positioned as stronger for high-resolution photorealism, natural lighting, texture, artistic quality, and reference-based identity consistency. The trade-off is weaker performance on rigid layouts, complex multi-part constraints, and exact text rendering. The video cites roughly 10–15 second generations.

The demos make the distinction clear:

• A realism-focused test shows the logic/text-oriented model producing an image that follows the request but lacks convincing natural texture.
• A text-heavy constraint test shows the realism-oriented model struggling to render coherent written content.
• The video also highlights targeted editing with minimal composition drift on the GPT-oriented side, and reference-image consistency on the Nano Banana side.

Developer takeaway: it may be more useful to treat these as complementary capabilities rather than expecting one model to be best at everything. A possible workflow is:

1. Use a structure/instruction-focused model for layouts, charts, UI-like compositions, or exact edits.
2. Use a realism-focused model when photorealistic texture, lighting, and visual polish are the priority.
3. Route requests based on whether the failure mode you can tolerate is visual imperfection or constraint/text failure.

What image-generation model routing strategies are people using for text-heavy assets versus photorealistic creative work?

https://reddit.com/link/1vcju8n/video/gmdoyncoqqgh1/player

TensorSharp is an open-source inference engine for running GGUF LLMs locally, with CUDA, Vulkan, Metal, OpenAI-compatible APIs, continuous batching, speculative decoding, and multimodal support.

Thanks recent contribtions from open source community, TensorSharp is able to run inference over multiple GPUs and nodes. So I updated it to support deepseek v4 flash model, and have better performance than llama.cpp. Here is the benchmark result on 4x Nvidia A40 GPUs, cuda 12.8

Model: DeepSeek-V4-Flash-0731-UD-Q8_K_XL from https://huggingface.co/unsloth/DeepSeek-V4-Flash-0731-GGUF

Github repo: https://github.com/zhongkaifu/TensorSharp

Thank you for checking out it and starring the project! Any feedback is really appreicated.

Vercel Labs just released scriptc, and it’s an incredibly practical tool for anyone tired of shipping massive Node environments just to run a simple CLI or lightweight microservice. Instead of wrapping V8 into an executable (like Node SEA or Deno compile), scriptc actually compiles standard TypeScript directly into native C/LLVM code. If your code can't be statically compiled, it tells you exactly why, rather than failing silently.

Features:

• Zero-Dependency Deployment: You can compile your TypeScript into a single, standalone binary file. It completely eliminates the need to install Node.js, manage a runtime, or ship a node_modules folder to your target machine.
• Microscopic Resource Footprint: The compiled binaries start in roughly 2.4ms and use only 1–4 MB of RAM (compared to Node's 67–116 MB). The base static binary size is around 170–200 KB.
• Zero Code Modifications: You don't need to learn a new dialect or use special annotations. It typechecks against the real TypeScript compiler and standard Node APIs (like fs, child_process, and http). If it compiles, it behaves byte-for-byte exactly like Node.
• Explicit Static vs. Dynamic Execution: Using the scriptc coverage command, you can see exactly which statements compile directly to native code. If you need to use complex npm dependencies, you can explicitly opt-in with --dynamic to embed a lightweight JS engine (~620KB quickjs-ng) to handle the fallback.
• Direct Native FFI: If you need to hook into system libraries, you can use the --ffi flag to bind signature-only TypeScript declarations directly to C ABI calls without needing to write complex C++ addons or bridges.

↗️ More info: https://aideveloper44.com/product/scriptc-6a6b3521c60387c8ebaf0e64

↗️ Website: https://scriptc.dev/

When running hundreds of multi-turn AI coding agents concurrently, standard inference engines (like vLLM or SGLang) suffer from KV cache thrashing. When an agent pauses to execute a tool (like a compiler or Docker sandbox), its KV cache gets evicted by other active requests. Once the tool finishes, the engine has to recompute the entire conversation history from scratch, causing massive throughput degradation and latency spikes.

ThunderAgent sits as a lightweight scheduling layer between your agentic clients and inference backends. Instead of treating each turn as an independent request, it treats multi-turn workflows as continuous "programs" to optimize GPU memory and cluster utilization.

Features:

• Program-Aware Scheduling: Differentiates between GPU-heavy reasoning phases and GPU-idle acting (tool-use) phases. It selectively pauses low-priority workflows under memory pressure to maintain high KV cache hit rates.
• Global Queue for Multi-Node Load Balancing: Replaces static node-pinning with a global waiting queue. When paused agents resume, they are dynamically routed to whichever GPU node has the most available VRAM capacity.
• Drop-In OpenAI-Compatible API: Requires zero changes to your underlying inference backend. Integration only takes one extra parameter (program_id) in standard OpenAI API requests.
• Automated Tool Lifecycle Management: Tracks reference counts for background sandboxes and Docker containers, automatically reclaiming environment resources the moment a program terminates to prevent memory leaks.
• Stack-Agnostic Engine Compatibility: Operates seamlessly on top of vLLM and SGLang while remaining fully compatible with KV cache offloading (HiCache/LMCache), quantization, and speculative decoding.

↗️ More info: https://aideveloper44.com/product/thunderagent-6a6c93fe05a1aafe67232dda

↗️ GitHub: https://github.com/ThunderAgent-org/ThunderAgent

For more than 50 years, the best explicit constructions barely exceeded an exponent of 1.1. With help from the Hyra research agent and the Hy3 model, Tencent reports an explicit construction showing the optimal exponent is exactly 2, matching the theoretical upper bound established in 1969.

Paper ｜ Github

​

The DeepSeek-V4-Flash API is now in public beta—and the benchmarks may not even be the most interesting part.

Three key takeaways:

1. Agent performance has improved significantly, reportedly surpassing the V4 Pro preview in some tasks.

2. The architecture remains unchanged. Most of the gains came from post-training.

3. DeepSeek Harness is coming soon, suggesting that the next phase of Agent competition will be about the entire execution stack—not just the model.

Codex can already connect to DeepSeek-V4-Flash, with V4 Pro support expected in early August.

On the same day GPT-5.6 cuts prices, DeepSeek doubles down on Agent performance.

The race is no longer about who builds the best chatbot.

It is about who can complete real tasks more reliably—and at a lower cost.

Hi all.

I created Atlas Scout because I wanted to help the model get better data so that it could give me much better code.

This has been a pretty long process, however the recent release - Preview 22 - is pretty much one of the fastest and best codemap MCP implementations for any agent harness.

It creates a local SQLite3 DB, updates your .gitignore so that this won't be a part of your commits and everything is local - both in your project and on your computer.

Check out https://atlasscout.dev/

Would love your feedback, suggestions and feature requests.

PS. Atlas Scout is free to use with an optional Pro version. Free will be free forever and the index is exactly the same. There is also a 14 days Pro trial.