Skip to contentSkip to stories

Updated

All AI news

Showing low-relevance items too. Hide low-relevance items

Sep 29

Sep 29Tue
  1. Google GemmaOfficialAI score20

    Google Gemma shows DiffusionGemma extracting scores via vLLM templates

    AIGoogle Gemma says DiffusionGemma can be turned into a Jev-like model by seeding a canvas with a response template in vLLM. The approach extracts confidence scores and probability distributions for yes/no, multiple-choice, and scored questions in a single denoising step.

  2. DeedyXAI score42

    Deedy shares a Claude Code workflow for AI video generation

    AIDeedy describes a video generation pipeline built around Opus 5.5 in Claude Code, routing image, video, audio, and TTS models through OpenRouter's single API key. The workflow adds reference-image consistency, animatics before full renders, a critic skill that screenshots and transcribes output for QA, and ffmpeg for most editing.

    Video from @deedydas's post
  3. DatabricksOfficialAI score22

    Databricks rolls out frontier models to employees on Day 1 via Unity Gateway

    AIDatabricks says it aims to give its employees the best models on launch day, quickly adopting new releases such as Opus 5.5 and GPT-6 Sol while tracking real-world usage and cost. Its AI engineering team uses Unity Gateway to manage access, spend, and model selection across thousands of employees, and to decide which models join its AI stack.

    Image from @databricks's post
  4. howie.seriousXAI score18

    Video explains Git in 100 seconds for the agent era

    AIHowie Serious (@howie_serious) shares a video titled "100 Seconds to Understand Git," aimed at explaining Git to everyone in the agent era. He notes most followers already know Git, but he made the video anyway and posted it.

    Video from @howie_serious's post
  5. Ahead of AI (Sebastian Raschka)BlogAI score43

    Language Models for Text Classification: From Bag-of-Words to Jev

    AISebastian Raschka traces text classification from bag-of-words models such as naive Bayes and logistic regression through pre-transformer neural networks, then sets up an analysis of the recently released Jev AI model. The article frames Jev as a general-purpose classifier that trades specialized accuracy for speed, cost, and breadth of tasks.

  6. Thomas WolfXAI score29

    Thomas Wolf calls a post simply "impressive"

    AIThomas Wolf, owner of the Hugging Face account, posted the single word "impressive" in response to a quoted post. The quoted post reports a new NanoGPT training record of 39.9s, down 27.7s from the prior 67.6s, achieved through per-flop optimizations such as sampled softmax and sparse updates.

  7. howie.seriousXAI score23

    Qwen3-VL 32B tags 40,000 Eagle images on a 128GB Mac

    AIA user ran qwen3-vl:32b-instruct locally through Ollama on a 128GB computer to auto-tag 40,000 images in their Eagle app. The image library was collected over 10 years and is intended as a personal asset base for Claude Code-made knowledge videos. The user says the use case makes the 128GB memory purchase feel worthwhile.

    Image from @howie_serious's post
  8. Suno BlogOfficialAI score12

    Three Essential Tips for Using EQ in Music Production

    AIEqualization (EQ) is one of the most widely used music production tools, and this guide offers three tips for using it well. The advice covers mixing by ear rather than by the visual curve, cutting problem frequencies before boosting, and placing EQ first in the effects chain so later effects process a cleaner signal. Suno Studio's per-track EQ supports multiple EQs per track and sharing of presets.

  9. Luma AI NewsOfficialAI score22

    AI Photo Editing Prompt Formula Preserves Color, Light, and Skin in Campaign Edits

    AIThe article presents a four-part prompt structure (action verb, target element, desired result, protection instructions) for AI photo editing, saying it preserves approved work across platforms. It identifies three common failure causes: unmatched light direction, stacked edits in one prompt, and vague visual language. It states that simple skin retouching takes 2-3 minutes versus 15-30 minutes manually.

Sep 28

Sep 28Mon
  1. vLLM BlogOfficialAI score54

    vLLM guide explains disaggregated serving for prefill and decode

    AIThe vLLM blog guide explains how separating prefill and decode, and moving tokenization to a CPU-only render tier, can keep token streams from stalling under load. In a two-L40S test on Qwen2.5-7B, collocated p99 inter-token latency reached 169 ms at 0.4 req/s while disaggregated serving stayed between 25 and 52 ms. The guide notes that the gain depends on fast KV cache transfer, and it includes setup code for NIXL-based serving and the render/derender API.

  2. SemiAnalysisBlogAI score43

    How GLM-5.3 Sparse Attention Affects HBM and Serving Costs on GB200, GB300, and MI355X

    AISparse attention cuts per-operation KV cache reads but does not reduce overall memory capacity, so top-k cache misses still depend on HBM. SemiAnalysis's InferenceX estimates GB200 at about $0.044 per million total tokens at 150 tokens per second, roughly 12% below MI355X running ATOM at $0.049. Neither system holds a uniform cost advantage across the tested 100, 125, and 150 tokens-per-second targets.

  3. LlamaIndex 🦙OfficialAI score30

    LlamaIndex says frontier VLMs still struggle parsing tax and W-series forms

    AILlamaIndex argues that frontier vision-language models still fail on real forms such as W-2s, 1040s, W-9s, and scanned W-4s, because forms require detecting every field, preserving section hierarchy, linking values to their exact boxes, and reading handwriting and checkmarks. The company's blog post details these failure modes and presents a custom cookbook for LlamaParse as a cheaper way to handle such forms.

    Image from @llama_index's post
  4. Google · Gemini appOfficialAI score16

    Edy's Grocer uses Gemini to scale recipes and plan shopping for catering

    AIEdy Massih, owner of Edy's Grocer in Greenpoint, Brooklyn, uses Gemini to scale family Lebanese recipes into catering batches, automate aisle-by-aisle shopping lists, and adapt menus for dietary restrictions. He says the tool handles the math and logistics, freeing him to spend less time on administrative tasks.

  5. Higgsfield AI 🧩OfficialAI score34

    Claude Opus 5.5 Drives 12 Laptops to Produce a Launch Video

    AIHiggsfield AI gave Claude Opus 5.5 access to 12 laptops, and from one prompt it split the work across machines using Computer Use and Higgsfield MCP. The system generated the visuals, built the animations, and assembled a fully editable After Effects project.

    Video from @higgsfield's post
  6. KhazixXAI score31

    Solo developer rewrites AIHOT with multi-model AI workflow in three days

    AIThe developer behind AIHOT rewrote the entire project over three days, then launched it after a 12-step AI-assisted workflow. The process used Claude Opus 5.5, Claude Fable 5.1, and GPT-6 Astra for distillation, rewriting, audits, testing, and a six-hour shadow-system rehearsal before cutover. The post frames this as an amateur's experience and includes a quoted suggestion to distill the source project into a feature document and rewrite it directly with the latest models.

    Image from @Khazix0918's post
  7. howie.seriousXAI score14

    Video explains how neurons and synapses shape learning and memory

    AIThe post introduces a knowledge video that explains learning at the neuron level, describing knowledge as circuits of connections between neurons rather than stored content. It highlights how signals switch between electrical and chemical forms at synapses, how review strengthens connections and adds myelin, and how unused connections get pruned, citing the cat-stripe experiment. It concludes the brain is not filled up but declines through disuse, drawn from Chapter 1, Section 2.1 of *Intrinsic-Drive Learning*.

    Video from @howie_serious's post
  8. Mastra BlogOfficialAI score29

    Mastra Publishes Guide to GDPR-Ready Agents with EU Hosting and Data Controls

    AIMastra's guide explains how teams can run agents under GDPR, with self-hosted deployments in any EU region or a platform environment created with --region eu. It covers PIIDetector redaction before data reaches the model, SensitiveDataFilter for trace fields, and retention and deletion handled in the team's own database. Mastra says it offers a DPA with EU Standard Contractual Clauses, a SOC 2 Type II audit, and no training on personal data.

  9. Kling AI BlogOfficialAI score9

    Kling IMAGE 3.0 Generates Basketball League Logo Concepts From Written Prompts

    AIKling AI's blog outlines a structured prompt method for basketball league logos, covering league identity, basketball symbol, style, colours, and composition. It provides six example prompts for professional, modern, youth, retro, minimal, and street styles, and shows how to generate concepts with Kling IMAGE 3.0 from text or reference images.

Sep 27

Sep 27Sun
  1. Felix RiesebergXAI score13

    Felix Rieseberg Rebuilds His Homepage Using Opus 5.5

    AIFelix Rieseberg, an Anthropic employee, says he remade his homepage with Opus 5.5 and pushed it hard, using it to create music, movies, textures, and Blender models. He says he is very happy with the result and links to his site.

    Image from @felixrieseberg's post
  2. KhazixXAI score14

    Distilling a legacy codebase into feature docs for a full rewrite

    AIKhazix suggests that rather than refactoring a messy legacy codebase, it may be more efficient to distill the source project into functional documentation and then have the latest model rewrite it in place. The post is a tongue-in-cheek remark, with a facepalm emoji expressing its sardonic tone.

    Image from @Khazix0918's post
  3. Xiaomi MiMoOfficialAI score62

    Xiaomi MiMo Explains Fixing Tool-Call Repetition in MiMo-V2.6 Models

    AIXiaomi MiMo reports that tool-call repetition in MiMo-V2.6 reached over 0.05% of responses across agent harnesses, causing stalled agents and wasted context. The team traced the cause to an RL flooding penalty set at 32 calls per turn, which missed smaller excess behavior, and replaced the approach with a specialized teacher distilled via MOPD. Repetition rates for both Pro and Flash dropped substantially, at roughly $90,000 versus an estimated $2.31 million for the alternative fix.

    Why it matters: The post traces an agent failure to a reward blind spot and compares the costs of two fixes, offering a transferable debugging method for RL-trained tool-calling models.

Sep 26

Sep 26Sat
  1. Sebastian RaschkaXAI score30

    Raschka's Reasoning from Scratch Covers Log-Probability Scoring and Self-Refinement

    AISebastian Raschka's fifth Reasoning from Scratch video explains log-probability scoring and self-refinement for LLMs. It covers token probabilities, PyTorch implementation, numerical stability, and a self-refinement loop evaluated on MATH-500, with the log-probability concept linked to cross-entropy loss in pre-training and distillation.

    Video from @rasbt's post

Sep 25

Sep 25Fri
  1. LMSYS OrgOfficialAI score38

    SGLang adds multi-item scoring for faster decision model serving

    AISGLang's /v1/score endpoint returns scores for exact requested labels such as Yes/No or A/B/C, and its multi-item scoring (MIS) computes shared context once while keeping candidates isolated. On Qwen3-8B, 16-candidate p95 latency dropped from 54.1 ms with Generate to 20.6 ms with MIS. On Qwen3-0.6B, MIS p95 stayed under about 100 ms as load rose, versus seconds for Generate and SIS.

    Image from @lmsysorg's post
  2. GitHub Blog · AI & MLOfficialAI score33

    How to build custom workflows with canvases in the GitHub Copilot app

    AICanvases in the GitHub Copilot app are customizable interfaces that you and the agent share, such as kanban boards, dashboards, or checklists. You create one by running /create-canvas and describing the workflow, what you can do in the interface, and what the agent can do. Changes made by either you or the agent appear immediately in the shared canvas, and completed canvases can be saved as reusable extensions.

  3. Google Cloud · AI & Machine LearningOfficialAI score43

    Google Cloud Introduces Managed Reinforcement Learning Fine-Tuning for Gemini Models

    AIGoogle Cloud has launched a managed reinforcement learning fine-tuning service (RLFT) that lets customers adapt Gemini models using a reward function they define instead of labeled answers. Users supply prompts and a reward function, while Google handles the RL infrastructure and proprietary model internals. The guide advises exhausting prompting and supervised fine-tuning first, and notes that RLFT suits tasks that are easy to score but hard to demonstrate.

  4. Amazon ScienceOfficialAI score38

    Amazon and Reactor build kernel path to real-time video generation on Trainium

    AIUsing the Neuron Kernel Interface, Reactor and Amazon's Neuron Science team built a kernel-centric path to real-time autoregressive diffusion video generation on Trainium. They addressed dynamic shapes, memory access patterns, and cache management, which are hard for generic compilers, and developed techniques intended to generalize across models.

Sep 24

Sep 24Thu
  1. MidjourneyOfficialAI score26

    Midjourney releases tutorial video on its new edit model

    AIMidjourney has published a video explaining how to use its new edit model for maintaining consistent characters and other edits. The post offers no further details on features, pricing, or availability.

    Video from @midjourney's post
  2. LlamaIndex 🦙OfficialAI score17

    LlamaIndex Explains Using Confidence Scores to Control Document Extraction Automation

    AILlamaIndex argues that extraction confidence scores are useful only when they help decide what can be automated and what needs human review. Using ExtractBench, the post compares extraction systems after confidence filtering, reporting that LlamaParse Agentic Plus reached 66.48% recall on expected fields at a 97% precision target. The post covers confidence cutoffs, precision versus recall, score coverage, score granularity, and human review volume.

    Video from @llama_index's post
  3. Microsoft Foundry BlogOfficialAI score40

    Foundry Agent Service adds egress policies to restrict hosted agent destinations in preview

    AIMicrosoft's Foundry Agent Service preview lets developers attach a named, ordered egress policy to a hosted agent, allowing only approved destination hostnames. The walkthrough uses an invoice agent, an Audit-mode RAI policy with a Deny default, and Allow rules for two finance and vendor hosts, configured outside the agent code. Network egress controls are preview features, not GA, with no preview SLA, and are not intended for production use.