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vLLM@vllm_projectOfficialAI score22AITogether 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.
vLLM@vllm_projectOfficialAI score34AIvLLM 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.
vLLM@vllm_projectOfficialAI score28AIvLLM'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.
vLLM@vllm_projectOfficialAI score46AIThree 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.
ClaudeDevs@ClaudeDevsOfficialAI score22AIAnthropic'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.
Sam Altman@samaOfficialSame storyAI score70AISam 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.
This story has a top pick“OpenAI rolls out GPT-6 and Intelligent UI to all ChatGPT users”
morgan@MorgantWillisXAI score57AIAWS 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.
Kush@kushbhuwalkaXAI score23AIDeveloper team open-sources fluffles-os on GitHub, presenting it as a hard lesson rather than the product at puffle.ai. The post says stateful server-hosted agents like Hermes proved unworkable, and the team's earlier fluffles agent, built as a near-unrestricted "god agent" on a Mac mini, was painful to harness because failure modes were unbounded. The team says it later ported to Eve, which let them focus on agent behavior instead of integration scaffolding, and plans a launch this week.
GitHub@githubOfficialAI score57AILocal 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.
Tinker@tinkerapiOfficialAI score31AIIdeaLens 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.
Georgi Gerganov@ggerganovXAI score31AIGeorgi Gerganov, creator of llama.cpp, said the project was showcased at a major Windows event. He credited years of community work for the software and hardware stacks now coming together, and expressed hope for wider adoption of local AI.
👩💻 Paige Bailey@DynamicWebPaigeOfficialAI score36AIGoogle 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.
OpenRouter@OpenRouterOfficialAI score38AICloudflare'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.
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.
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.
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@AravSrinivasXPickAI 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.
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.
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.
