Xiaomi just made MiMo V2.5 the cheapest vision model on the market. 97% price cut on cache hits. New pricing (effective today): Pro V2.5: • Cache hit: $0.0036/M input (was $0.435) • Cache miss: $0.435/M input • Output: $0.87/M Base V2.5: • Cache hit: $0.0028/M input • Cache miss: $0.14/M input • Output: $0.28/M At cache hit rates, Pro is now 97% cheaper than before. Base is practically free at $0.0028/M. Why this matters: MiMo V2.5 is a 310B MoE (15B active) vision-language model with 1M context and MIT license. It tops Claw-Eval for agentic vision tasks. At these prices, you can run vision pipelines that were previously uneconomical — document processing, UI automation, real-time visual agents. I've been using it on our stack for visual understanding tasks. At cache hit pricing, it beats every vision model on cost by a wide margin. OpenRouter hasn't updated their pricing yet ($0.435/$0.87 still showing). Direct API is the move if you have predictable cache-hit workloads. Source: platform.xiaomimimo.com/docs… #AI #VisionModels #OpenSource #PricingWar

Are you just downloading vision models or actually understanding how they process images? In this clip from @goinggodotnet 's Ultimate AI Workshop, Bill breaks down the "missing link" in vision models: projection files MTMD. 👉 youtu.be/dpcmUgtYqGA The reality: → Vision inference requires a specific stack of model files, projection files, and correct formatting, not just a single download. → Vision models need projection files to translate raw image bytes into something the model can actually "see." → F16 is the gold standard for inference; don't waste resources on massive F32 or training-specific BF16 files. → Navigate Hugging Face with intent: If you don't grab the right sidecar files, your model will silently fail on image tasks. Stop treating vision models like text-only systems. It's time to master multimodal architecture. 🛠️ #AI #VisionModels #AIEngineering #LocalAI #HuggingFace #Multimodal

📸 Gemma 4's vision capabilities are seriously impressive. Google's open model now rivals closed-source alternatives on image understanding tasks — and it's built for on-device deployment. Multimodal AI that doesn't need the cloud? That's the unlock we needed. #Gemma4 #AI #VisionModels #GoogleAI

🚀 Hiring: Software Engineers (all levels) — Coram AI 📍 Remote (Global) | 💼 Not provided | 🧑‍💻 AI / Physical world | 🕐 Posted recently Coram AI is hiring Software Engineers (all levels). They are building the intelligence layer for the physical world — turning real-world data into intelligent, actionable systems as a high-agency, AI-first team at the frontier of physical AI. 🔑 Role and impact Problems solved are not theoretical — they evolve with cutting-edge AI breakthroughs and create real-world impact. Engineers take ownership, ship fast, and build systems operating across hardware, cloud, and AI models. 🛠 Core areas of work — Edge of AI progress: LLMs, modern vision, multimodal models for extracting insights — Scalable streaming systems and distributed architectures — Architecting hybrid systems spanning edge devices and cloud infrastructure Ideal for those excited by challenging engineering with meaningful outcomes. 📩 To apply: Reach out directly via DM to Purushotham Santhati on LinkedIn (linkedin.com/in/purush7) or connect with Vishwajitt 🧠 — follow their official process and contact the company directly. 🔗 Original post: linkedin.com/posts/purush7_w… ⚠️ DYOR — the poster doesn’t verify every job. If someone asks to run files (even from GitHub) or only replies via Telegram — 🚩 likely a scam. ❗️Not hiring. Just sharing real Web3 jobs with low competition — daily, for all levels! 💡 For Interns & juniors → t.me/crypto_vazima_english 💼 Mid/senior jobs → t.me/web3_jobs_crypto_vazima #SoftwareEngineer #Hiring #AI #Engineering #TechJobs #RemoteJobs #ArtificialIntelligence #MachineLearning #LLM #VisionModels #DistributedSystems #EdgeComputing #Cloud #PhysicalAI #JobOpening

After 2 years probing #visionmodels at the #KempnerInstitute, @thomas_fel_ reflects on what he’s learned—and what pieces of the #interpretability puzzle remain hidden—as he heads to @GoodfireAI. Read the interview: bit.ly/4aEBzmp 🎙️🧩

It’s 2026 learn RAG LLMs Agents Multi-Agents Embeddings VectorDB Fine-tuning LoRA QLoRA Prompting SystemDesign LangChain LlamaIndex Transformers Tokenization Inference Distillation Quantization RLHF DPO SFT Guardrails Eval Benchmarks RedTeaming AI Safety OpenWeights OnDeviceAI EdgeAI FederatedLearning Diffusion GANs VisionModels OCR ASR TTS Multimodal GraphRAG KnowledgeGraphs Neo4j Elastic FAISS Pinecone Weaviate Milvus Chroma Postgres pgvector Supabase Docker Kubernetes Serverless AWS GCP Azure Vercel Cloudflare Rust Go TypeScript Python Node FastAPI NextJS React Tailwind tRPC GraphQL REST WebSockets gRPC Redis Kafka WebRTC OAuth JWT ZK zkSNARKs zkSTARKs MPC TEE Homomorphic EVM Solidity Foundry Hardhat AccountAbstraction ERC4337 Rollups Optimistic ZKRollups EigenLayer Layer2 DeFi AMMs Perps MEV IntentBased Oracles Chainlink IPFS Arweave Filecoin Bitcoin Lightning BCH Ordinals Runes WalletConnect Passkeys DID SSI ZeroTrust DevSecOps CI/CD GitHubActions Testing Vitest Playwright Cypress UnitTests IntegrationTests LoadTesting Observability Prometheus Grafana OpenTelemetry Sentry FeatureFlags A/BTesting Analytics SEO Growth ColdEmail Copywriting Storytelling PersonalBrand Distribution Community DevRel OpenSource Licensing Roadmaps PRDs UX UI DesignSystems Figma Accessibility MobileFirst PWA iOS Android Flutter ReactNative Swift Kotlin DataEngineering ETL Airflow dbt Snowflake BigQuery Pandas NumPy PyTorch TensorFlow JAX CUDA WebGPU EdgeCompute CDN Caching RateLimiting API Design Microservices Monorepo Turborepo Nx FeatureStores VectorSearch SearchRanking Recommenders TimeSeries Backtesting Quant HFT RiskModeling Privacy Compliance GDPR SOC2 BugBounty ThreatModeling IncidentResponse ProductMarketFit MVP Bootstrap Fundraising PitchDeck Angel VC Revenue Monetization SaaS B2B B2C NoCode LowCode Automation Zapier n8n Webhooks CLI DX

Learn how to build a low-cost WhatsApp bot that analyzes images using AI vision models like Llama and GPT-4V, with Python and MongoDB. - hackernoon.com/how-i-built-a… #visionmodels #pythonwhatsappbot

👁️ Google just launched Agentic Vision for Gemini 3 Flash This feature turns image understanding from a static process into a dynamic one. Instead of the usual: “Look at the image and answer” Gemini can now do: “Look at the image, work with it, and then answer.” Think–Act–Observe for vision Agentic Vision introduces an explicit loop: Think — analyze the image, build a plan Act — run code for image processing, detection, calculations, etc. Observe — feed results back into context and use them for the final response So the model doesn’t just guess — it operates on the image. Example: counting fingers. Instead of replying “5” immediately, the model detects each finger with bounding boxes, counts them, and then produces the answer. A simple example, but it captures the shift: the image becomes a kind of visual scratchpad for reasoning. Where it shines complex tables small details structured visual data tasks requiring precise counting or localization Google reports an average 5–10% metric improvement over vanilla Gemini 3 Flash. Agentic Vision is already available in both the API and AI Studio. More details: blog.google/innovation-and-a… #AI #Gemini #ComputerVision #AgenticAI #Multimodal #LLM #Google #AIEngineering #MachineLearning #VisionModels

PolyRead is an instant way to get a pic from camera or upload, gets OCR of the image, from state of the art Google and ChatGPT vision models #multimodal and translate to more than 50 human languages: polyread.cxloop.co #OCR #Gemini #ChatGPT #NLP #VisionModels

Vision Models Vision Models help AI see and understand images — objects, text, scenes. Used in face unlock, product recognition, and self-driving tech. When combined with language → Multimodal AI (GPT-4V, Gemini, Claude Vision) #AI #VisionModels #Multimodal #ComputerVision

Auto-annotate PDF images with local vision models (data stays private) 🖼️ Images in PDFs like charts, diagrams, and figures are invisible to search and analysis. Manually writing descriptions for hundreds of figures is impractical. You could use cloud APIs like Gemini or ChatGPT, but that means API costs at scale and your documents leaving your infrastructure. Docling runs local vision language models (Granite Vision, SmolVLM) to automatically generate descriptive annotations for every picture in your documents, keeping data private. Key benefits: • Privacy: Data stays local, works offline • Cost: No per-image API fees • Flexibility: Customizable prompts, any HuggingFace model 🚀 Full article: bit.ly/4ioeuXm ☕️ Run this code: bit.ly/3XgRfVt #Python #DataScience #MachineLearning #VisionModels

Crowd Map: Detects up to 5,000 people simultaneously with 2× extended range. Reliable in rain and at night! Sharper, dependable crowd management from Dahua Xinghan. tiny.cc/xinghanweb #CrowdMap #DahuaXinghan #SmartCity #CrowdManagement #VisionModels

Second day on Symbiote and things are clicking. Started with Phi3-mini (3.8B) for generating test steps decent, but too hit-or-miss for reliable automation🧐. Now pivoting to vision models so the agent can literally “see” the UI and act like a human tester. Tested LLaVA → instructions solid, vision still fuzzy on dense UIs. Today I am gonna give BakLLaVA & Moondream try. Drop your favorite open-source vision model below (bonus if <7B and actually good at UI screenshots). Let’s crowd-source the best eyes for autonomous testing agents 👀 #AITesting #LocalFirst #OpenSourceAI #VisionModels #LLaVA #Moondream #BakLLaVA #TestingTools #IndieDev #BuildInPublic #WebDev #TypeScript #AI #Automation #Builders #Python

NEW: 🐰Into the Rabbit Hull — Part II 🚀 @Napoolar @WangBinxu & collaborators ask: Is the linear view of DINOv2 enough to explain how deep vision models actually organize information? Read more: bit.ly/4rlgJyH #AI #VisionModels #Interpretability

WizTracking: Stop losing targets! Dahua Xinghan AI uses Multi-posture Tracking for continuous detection, even when the target’s posture changes. Always detected, always secure. tiny.cc/xinghanweb #WizTracking #DahuaXinghan #SeamlessSecurity #VisionModels

Ridicoulous discussion. Both are needed: LeJEPA a fantastic theortical work that simplifies training of visionmodels immensely as validated using pytorch

A Lightning tour of Vision Language Models 🧠👀🛡 #ai #vision #ml #visionmodels

👀 Built to read like a human. DataWoz’s layered processing engine blends OCR and vision-language models to achieve human-level accuracy and dependable document intelligence. #AI #OCR #VisionModels

AI WDR: Flawless images, automatically. Dahua Xinghan Vision Models use scene adaptivity to intelligently configure WDR, ensuring clear facial details and balanced exposure in any light. See the difference: tiny.cc/xinghanweb #DahuaXinghan #VisionModels

มาสด้า เปิดโฉม Vision Models ยนตรกรรมต้นแบบ 2 รุ่น ครั้งแรกของโลก ในงาน Japan Mobility Show 2025 #มาสด้า #VisionModels #รถต้นแบบ #JapanMobilityShow2025 #BTimes btimes.biz/whatsup/ม…