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OpenAI@OpenAIOfficialAI score26AIOpenAI says users can create interactive tools directly in the ChatGPT conversation, such as a game or a budget planning calculator. The post provides no further details on availability, limits, or implementation.
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.
Georgi Gerganov@ggerganovXAI score44AIllama.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.
Liquid AI@liquidaiOfficialAI score25AILiquid 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.
Unsloth AI@UnslothAIOfficialAI score23AIUnsloth 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.
0xMarioNawfal@RoundtableSpaceXAI score23AIAn 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.
Liquid AI@liquidaiOfficialAI score40AILiquid 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.
Liquid AI@liquidaiOfficialAI score38AILiquid 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.
Liquid AI@liquidaiOfficialAI score23AILiquid AI's d1-3B ranks first among models under 10B parameters on the Decision Index v0.2.1, a benchmark for structured decision-making. Built from LFM2.5-VL-3B, it makes decisions from text and images in a single pass. It is suited to reranking, agent guardrails, and visual inspection.
Liquid AI@liquidaiOfficialAI score52AILiquid AI released Open d1, two open-weight multimodal models in its d1 decision model family. The d1-3B model supports text and vision, while d1-omni-600M supports text plus image or text plus audio. The source says the models are meant for real-time decision making across data centers, RTX workstations, and Jetson edge devices.
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).
Nous Research@NousResearchOfficialAI score38AINous Research has raised a Series B, as reported by the Wall Street Journal, to bring its Hermes Agent to new frontiers and build a mobile app. Investors include Nvidia, M12, Samsung Next, Robot Ventures, USV, Y Combinator, and Menlo Ventures, among others.
Daniel Han@danielhanchenXAI score48AIUnsloth now lets users train their own decision models locally on just 3GB of VRAM by fine-tuning Qwen, Gemma, and Llama with a Clef head. The post says this raises accuracy from 30% to as high as 78%, and the tool is available through Unsloth Desktop.
Aravind Srinivas@AravSrinivasXAI score62AIPerplexity 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.
Unsloth AI@UnslothAIOfficialAI score40AIUnsloth released an open-source method to fine-tune LLMs into decision models that run locally, lifting Qwen3.5 0.8B's aggregate accuracy from 20.7% to 74.3% across three decision benchmarks. The team used a Clef head with LoRA (r=64) for one epoch on just 4GB VRAM, with the approach applicable to models such as Qwen3.8 and Gemma 4. A guide and notebooks are available on the Unsloth documentation site and GitHub.
Perplexity@perplexity_aiOfficialAI score27AIPerplexity 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.
Perplexity@perplexity_aiOfficialAI score45AIPerplexity has released pplx-embed-v2-late, two late-interaction embedding models that retrieve text, images, and pages within a shared embedding space for cross-model querying. The company says both models achieve frontier performance and are publicly available on Hugging Face.
LlamaIndex 🦙@llama_indexOfficialAI score47AILlamaIndex announced OpenDocRouter, a single API that routes document parsing requests to any of 10 frontier and open-source models at launch, including Claude Opus 5.5, Gemini 3.8 Flash, GPT-6 Luna, MinerU2.5-Pro, and PaddleOCR-VL-1.6. Users can switch models in one line with the same request and markdown output, and each model is scored on ParseBench for quality and cost. Pricing is per-token, failed pages are not charged, and the service costs $0.86 to $48.82 per 1,000 pages depending on the model.
AIMicrosoft Research Asia has open-sourced Agent Lightning v1.0, a roughly 3,500-line agentic RL framework that trains the same agent harness used in deployment. In an end-to-end coding agent pipeline, Qwen3.5-9B rose from 41.8% to 56.4% Pass@1 on SWE-bench Verified using about 6,000 training samples. The framework runs agents as standard Kubernetes jobs without paid commercial sandbox services.
Why it matters: The source shows how training with the deployed agent harness avoids rebuilding agents, and reports concrete SWE-bench Verified gains from about 6,000 samples.
Ai2@allen_aiOfficialAI score36AIAi2 is making new Stage 1 checkpoints available, with the byte-level components already trained and the original model's weights left unchanged. Researchers can use them to test new architectures and train the full system without repeating that initial stage.
Ai2@allen_aiOfficialAI score20AIAi2 introduces Bolmo, a model that operates on bytes rather than tokens, which the team hopes will lead to larger models adapting how they represent information across languages and domains. Because bytes can also represent images and audio, the approach could eventually extend beyond text.
Ai2@allen_aiOfficialAI score39AIAi2 converted Qwen 3 8B and Llama 3 8B into byte-level models named Bwen 8B and Blama 8B. Both come close to matching their source models' performance in Ai2's evaluations, and Bwen 8B outperforms Bolmo 7B across its aggregate evaluation suite.
Ai2@allen_aiOfficialAI score38AIAi2's paper on retrofitting language models to operate over bytes, the approach behind Bolmo, has been accepted to Nature. The team is also releasing new checkpoints that extend the method from Olmo to Qwen and Llama.
Demis Hassabis@demishassabisXAI score34AIIsomorphic Labs has joined the Virtual Biology Initiative (VBI) as a founding member, helping build an open resource for global researchers to accelerate understanding and treatment of disease. Demis Hassabis, owner of the account and Google/Gemini-affiliated, called applying AI to medicine and human health the most important thing AI can be used for and welcomed the partnership with CZI and VBI.
Lucas Beyer (bl16)@giffmanaXAI score36AILucas Beyer praises Reality Check, a new leaderboard for benchmarking VLA and related robot manipulation models. Half of its tasks are fully open, while the other half are held out to detect benchmaxxing by future model versions. The companion post from Nicolas Keller describes the launch as the first public robot manipulation benchmark, built on 14,400 real-world rollouts across four models.
AIGitHub Copilot CLI version 1.0.94-0 lets users run /model to discover supported models from a running local Ollama instance alongside configured and GitHub Copilot cloud models. Discovered models are not added automatically; users choose one, review its provider and endpoint, then confirm Add and use for this session or Add without switching, and models must support tool calling and streaming. Choosing a local model does not enable offline mode or disable GitHub telemetry, and COPILOT_OFFLINE=true remains a separate explicit setting.
Andrew Curran@AndrewCurran_XAI score27AINous Research has raised $90 million at a $1.5 billion valuation, according to Andrew Curran's post. He praises the team and says Hermes has been a major success for the company.
elvis@omarsar0XAI score44AINVIDIA's VERA turns benchmark trajectories into over 9,000 restartable sandboxes with rubric scoring and updates both model weights and the agent harness together. A harness edit is kept only if it adds at least 5 points on the development set, and a checkpoint is rejected if its score drops more than 20%. At 27B, the co-evolved agent scores 71.6 on AutoCoWorkBench, above Claude Opus 4.8, and the environment corpus is open-sourced.
ComfyUI@ComfyUIOfficialAI score34AIComfyUI says a Blender render can supply the camera path, character positions, attack trajectories, timing, and choreography for AI video. The workflow then uses reference images to replace the blockout with final characters and an anime look.
Google Gemma@googlegemmaOfficialAI score41AIGoogle's Gemma account promotes research showing how AI can help scientists secure the global food supply, linking to a Gemma 4 and BOTANIC-1 demo on Hugging Face. The post also offers the BOTANIC-1 download and the accompanying paper.
elvis@omarsar0XAI score44AISmallest AI's Pulse ranks first on the Diarization + ASR track of Voice Arena's Diarization Bench with a 24.4% DER, where lower is better. The next system in that track scores 40.7% DER, and Pulse misses 4.4% of reference speech, the lowest in the track.
AIMistral has released Mistral Large 4, nicknamed Le Chonk, a 1 trillion-parameter model it says can be used and customized by anyone. It is in preview, with a final version due by the end of the month, and is optimized for coding and cyberdefense as well as manufacturing, finance, and electrical engineering tasks. Mistral claims it is the most capable open-weight model developed outside China and says it was trained from scratch rather than through distillation.
AIStacklok, founded by Kubernetes creators Craig McLuckie and Joe Beda, has released Mecatl, an open source cloud-native harness for coding agents on GitHub. Mecatl keeps the agent loop separate from the client, model provider, state store, and execution environment, and moves tool calling, session management, and memory into manageable systems. The article also covers ToolHive, an MCP platform, and an AI Gateway that is not yet open sourced, with a commercial enterprise control plane tying the pieces together.
Mistral AI@MistralAIOfficialAI score22AIMistral Large 4 scored highest on AutomationBench, a benchmark of 657 business workflows across simulated workplace apps including Gmail, Google Sheets, Slack, and Salesforce. It finished ahead of Kimi K3, MiMo-V2.6-Pro, and DeepSeek V4 Pro.
AINVIDIA reports that fine-tuned Nemotron models reached gold-medal level at both IOI 2026, scoring 535.4 out of 600, and IMO 2026, scoring 30 out of 42. The IOI run was a live, unofficial, unsupervised benchmark, while IMO proofs were graded by official IMO graders. The post also releases checkpoints, datasets, a new 200-problem benchmark, and inference pipelines on Hugging Face and NeMo-Skills.
Why it matters: The post traces how SFT, RL, and a generate-verify-refine loop turned Nemotron into gold-level specialists for IOI and IMO, with the training and inference details shared.
Teknium 🪽@TekniumXAI score36AIDevelopers are running Hermes on devices such as LilyGO watches, AIPI Lite, desk gadgets, and old Android phones after the Hermes Gadget open SDK and demo were released three days ago. The post credits @NousResearch and says more boards are landing on main through contributor PRs, with the SDK available on GitHub.
Orange AI@oran_geXAI score35AIOrange AI released "podcast-to-article," a free MIT-licensed open-source skill that reorganizes podcast transcripts by topic rather than speaker timeline. The author says it keeps every number and original quote while cutting off-topic tangents, and that early readership and engagement were good.
AIReflection AI announced Beam, a model trained end-to-end from scratch that appears to advance the Western open frontier on coding and agentic tasks. Mistral then released Mistral Large 4, a 1 trillion-parameter natively multimodal model with 49 billion active parameters, though the piece says neither model yet matches leading Chinese open-weight models.
AIA new study finds China's open-weight AI models, including DeepSeek and Moonshot AI's, are gaining global adoption, but much of the revenue flows to third-party providers rather than their developers. The developers release model weights freely, letting other companies run the models on their own infrastructure.