Open source

AIA Hugging Face blog author used the ML-Intern agent in HuggingChat to build six small models by writing detailed prompts that specify datasets, base models, baselines, smoke tests, and spending limits. The projects include a citrus disease vision-language model, a Huggy character LoRA, a camera-angle LoRA, a doodle-to-object LoRA, a 0.8B prompt rewriter, and a 4-step distilled Agate model, with total compute cost of about USD 103. Each project's prompts and public models are linked from the post.

Why it matters: The author shows how prompt structure, baselines, smoke tests, and budget caps shape an agent-driven training workflow, with per-project costs given.

AITeknium, a Nous Research affiliate, posted only the word "hello" on X. The quoted post from Nous Research announces a Series B raise to advance Hermes Agent and build a mobile app, with investors including NVIDIA and Samsung Next.

AIDeveloper Brandon Thomas's Artcraft has launched seven open source apps in Rust that aim to replicate the interfaces and tools of Adobe Photoshop, Illustrator, Premiere, Lightroom, After Effects, InDesign, and Acrobat Pro. Thomas said he used Anthropic's Claude Opus 5.5 to build the clean-room replacements, with WebAssembly versions available for browser use. The apps remain in a "super early alpha" state, and commenters have pointed out many current shortcomings.

AIPerplexity's open-weights multimodal decision model, pplx-decider-v1.1-27b, is now available on OpenRouter. It accepts text, JSON, or images and returns typed answers with probabilities, priced at $0.02 per million input tokens with free output.

AISnorkel AI is expanding its Open Benchmarks Grants tenfold to a $30M commitment to fund more diverse, robust, and continuously updated open AI benchmarks. The program adds an Open Benchmarks Red Team to test and strengthen those benchmarks, plus a Snorkel Research Fellowship for independent researchers developing new evaluation methods. The source says OBG-funded benchmarks have appeared on model cards from every major frontier lab.

AIPerplexity's open-weights multimodal decision model, Decider v1.1, is now available on OpenRouter. It accepts text, JSON, or images and returns typed answers with probabilities, priced at $0.02 per million input tokens with free output. Perplexity says it scores highest on Hugging Face's new Decision Index 0.3 benchmark and costs half as much as v1.

AITogether with DeepSeek's model and kernels and NVIDIA's collaboration, vLLM reports that time to first token (TTFT) drops nearly 70% at roughly 100K throughput. The post credits Inferact and the vLLM community for the work and thanks SemiAnalysis for AgentX.

AIvLLM has integrated DeepSeek's MegaAttention, which uses NVFP4 KV cache and is 45% smaller, alongside Mega-mHC, Mega-Gate, and DeepSelect kernels. Its own fusions include a CuTe-DSL fused WO-A that lowers ITL by up to about 6–7%, and sparse MQA logits that run 14–23× faster per layer at 512K context.

AIvLLM's bounded sliding-window attention (SWA) replay reruns only each request's last 128 tokens after a prefix hit, instead of replaying 40 × 128 tokens. With CUDA graphs, prefill compute drops 30–40% for layers 21–39.

AIThree weeks after launch, vLLM's serving of DeepSeek-V4.1-Flash runs 1.9× faster at low concurrency and delivers 5.3× the throughput at 150 TPS per user on SemiAnalysis AgentX. The post offers interactive figures explaining how these gains were achieved.

AIAnthropic's ClaudeDevs account recommends compatible browser drivers from Browser Use, Browserbase, E2B, and Daytona for computer-use tooling. Developers can also write their own driver based on the example drivers in the quickstart. Quickstart and documentation links are provided for the computer-toolset setup.

AISam Altman reposted an OpenAI announcement that GPT-6 and Intelligent UI are rolling out in ChatGPT for everyone. According to the quoted post, Intelligent UI produces fast, interactive answers with visual explanations and on-the-spot tools for tasks.

AIAWS has open sourced Strands Box, a local sandbox for AI agents. The author points readers to a blog post by Marc Brooker that covers design tradeoffs, how OS-level containment and Dogwood enable richer policies, and AWS's investment in agent safety.

AILocal sandboxing for GitHub Copilot is now generally available. It lets Copilot run commands in an isolated environment with controlled access to files, networks, system capabilities, and credentials. Enterprise teams can also centrally manage policies, and the feature is available in GitHub Copilot CLI, the GitHub Copilot app, and @code.

AIIdeaLens is a detector that identifies whether the ideas in a text came from a human or an AI, rather than judging the prose alone. On mixed-provenance benchmarks, it reached 81.3% average idea-detection accuracy, versus 25.4% for ProseLens and 25.9% for Pangram 4. The model, code, and data are open-sourced, and it was trained on Tinker.

AIGoogle released EmbeddingGemma 2 on Hugging Face, and Paige Bailey called it a step toward open models for open science. The post links the model and cites earlier EmbeddingGemma-based projects, including medical, geographic, oncology, and PubMed embedding models.

AILiquid AI released two open-weight multimodal decision models, d1-3B and d1-omni-600M, which return probability answers in one forward pass with zero output tokens. d1-3B scores 48.57 on Decision Index v0.2.1 and answers one question in 8 ms on an RTX 4090, while the models are licensed free for commercial use below $10 million in annual revenue.

AICloudflare's open-source Clef (27B) and Clef Flash (9B) decision models are available on OpenRouter. They accept text, JSON, or images and return typed answers with probabilities rather than generated text. Pricing is $0.24 per M input tokens for Clef and $0.09 per M for Clef Flash, with output free.

AIClaude Code v2.1.293 adds Claude Haiku 5.5 (claude-haiku-5-5), now the default Haiku model on the Anthropic API, with 1M context and pricing of $0.10/$0.50 per Mtok ($0.50/$2.50 for prompts over 100K). The release also adds agentType to the subagentStatusLine payload and isDeferred to $.tool.register, and fixes numerous issues including a memory leak in HTTP MCP connections.

AIPreference Model is open-sourcing Karotte, the framework it uses to build reinforcement learning environments that resist reward hacking, including defenses like killing stray processes before grading and rejecting grader-crashing files. The framework has been hardened through more than a million evaluation runs and controlled red-teaming. The company focuses on machine learning engineering tasks for leading labs, and a16z says it is partnering with Preference Model and its founders, Jennifer Zhou and Ning Cao.

AIllama.cpp can distribute inference across heterogeneous devices through the ggml RPC backend, according to Georgi Gerganov. He says it is currently an advanced setting, but he expects it to become more accessible to regular users over time. A related post reports MiMo 2.6 Flash running across an RTX 6000 GPU and an M5 laptop over 10 GbE at about 40 tokens/sec.

AILiquid AI and NVIDIA Robotics announced a collaboration around Liquid AI's models on NVIDIA hardware. The main post is brief and gives no further technical detail or figures.

AIUnsloth says users can train their own Decision Model on as little as 2.5GB of VRAM using small models such as Laya. Training is done through a UI, with a video tutorial and guide linked from the post and the project's GitHub repository.

AITerse, a Claude Code plugin, enforces a shorter reply style by cutting filler, slogans, metaphors and first-person narration. The author says it reduces replies by 46% on Fable 5.1 and 54% on Opus 5.5, measured on 20 prompts against a real codebase. The plugin installs from the Claude directory or GitHub, and its rules apply to replies, documents, commits and subagents.

AIAn open-source GitHub repository called AI Engineer Headquarters offers a structured curriculum for aspiring AI engineers. According to the post, it covers fundamentals, large language models, RAG, fine-tuning, tool calling, and agentic workflows.

AIReflection AI and Mistral each unveiled new open-source models this week, aiming to beat other Western open models, though they trail top Chinese and closed systems on prominent benchmarks. Reflection CEO Misha Laskin says the target is regulated industries and governments that cannot or will not use Chinese models. The outcome depends on whether businesses and agencies accept less advanced models for some tasks in exchange for lower cost and more control.

AILiquid AI has released its d1 decision models, with d1-3B recommended for high-quality text and vision tasks and d1-omni-600M offered as an experimental option for smaller footprints. The weights are available for download, fine-tuning, and local deployment.

AILiquid AI's Open d1 models run across NVIDIA DGX, RTX, and Jetson hardware, with day-one llama.cpp support for deployment anywhere. Measured one request at a time, the d1-3B model's single-question latency is 8 ms on an NVIDIA RTX 4090, 16 ms on Jetson AGX Thor, 26 ms on Jetson AGX Orin 64 GB, and 50 ms on Jetson Orin Nano.

AILiquid AI released two open-weight decision models, d1-3B and d1-omni-600M (experimental), built on its Liquid Foundation Models and available on Hugging Face. d1-3B scores 48.57 on the Decision Index 0.2.1, the highest among decision models under 10B parameters, and answers a question in 16 ms on an NVIDIA Jetson AGX Thor and under 50 ms on a Jetson Orin Nano. The models support text and images (d1-3B) or text with image or audio (d1-omni-600M).

AIPerplexity is open-sourcing pplx-embed-v2-late, multi-vector embedding models for text and images in one shared space, in 9B and 0.6B sizes. The 9B model can index multimodal data, the 0.6B model can run queries on device, and PDF pages can be searched without OCR. The author reports 92.4% on MADQA and 64% on BrowseComp+, with weights available on Hugging Face.

Why it matters: Two open-weight multi-vector models share one space for text and images, with a 0.6B on-device option, a useful comparison for building multimodal retrieval.

AIPerplexity has published both pplx-embed-v2 models on Hugging Face, with the collection available at the linked URL. The post provides no further details on model sizes, benchmarks, or capabilities.