This is a genuine, specific observation with self-aware irony baked in — you notice the AI-saturation, call out that the posts themselves are often AI-written, and catch yourself mid-rant realizing this post is also about AI. That's an actual, relatable insight, not a recycled LinkedIn platitude.

DeepEval is an open-source Python framework for evaluating LLM applications such as agents, RAG pipelines, and chatbots.It helps you turn quality checks into repeatable tests by combining Pytest-style test cases with ready-to-use evaluation metrics.Key features:• Ready-made metrics – evaluate answer relevancy, faithfulness, hallucination, agent task completion, and more.• Pytest-style workflow – define test cases and run them from the CLI with `deepeval test run`.• End-to-end and component evals – assess the full application or individual traced components.• Flexible evaluation models – use your chosen LLM, statistical methods, or local NLP models, depending on the metric.• Dataset and CI support – generate single- or multi-turn synthetic datasets and run evaluations in any CI/CD environment.

⚡ TikTok search keeps growing — Gen Z now discovers products through short videos more than traditional search. ⚡ Reddit ads are getting serious attention — Brands are focusing more on niche communities and authentic conversations. ⚡ LinkedIn is pushing AI content tools — Creating posts is becoming easier… but standing out is getting harder. ⚡ YouTube Shorts adds new AI features — Faster editing + faster publishing = more competition. ⚡ Canva launched powerful AI design updates — Even non-designers can now create high-quality visuals quickly. ⚡ Adobe reports massive AI traffic growth — AI-generated referrals are changing how websites get discovered. ⚡ Meta is testing AI business assistants — Customer support and repetitive tasks are becoming automated. ⚡ Shopify expands AI shopping automation — Personalized shopping experiences are becoming the new normal. The biggest lesson? Marketing is shifting from: "Who creates the most content" Which update do you think will impact marketing the most in 2026? 👇 ♻️ Save & Repost to inspire others.

I've been comparing ways to give agents web access, and what sets PinchTab apart is a security boundary. It drives real Chrome behind an HTTP API and MCP server, but defaults the browser to a local-only allowlist, so the agent can't reach the public internet until you widen it, and extracted page text is framed as untrusted content. 9.5k stars, MIT.hashtag

Shadowbroker pulls 60+ live intelligence feeds onto one map, then lets an AI agent work all of it through a single channel instead of wiring up each feed separately. The agent reads every layer and runs the lookups itself. The risky queries stay on the server, so your browser never holds the keys or reaches outside APIs directly. 10k stars, AGPL-3.0.

Fifteen years ago, Coursera and online courses changed education. It worked better than almost anyone expected, expanding access by opening up where you can learn. But how you learn remains largely the same as it has for centuries: it is still one-size-fits-all, taught the same way to each person who shows up. We now have an opportunity to change how learning happens. With advances in AI, we can now build a custom learning guide for each person. We will turn learning from one‑to‑many to one‑to‑one. I'm starting LearnVector to invent this next generation of learning. We are starting with a $100M investment from Coursera, and plan to collaborate closely with Coursera and Udemy. Good learning needs much more than just a chatbot. Research shows that chatbots without guardrails harm learning. They help complete tasks and enable students to do better on homework. But cognitive offloading to a chatbot results in them being less skilled. And, you cannot always trust what a chatbot tells you. In contrast, LearnVector will plan a path with you, adapt to how you learn, and patiently stay with you until you’ve mastered new skills. One thing has not changed in all this time. People want learning they can trust: material that is accurate, relevant, and worth the effort you put into it. Anything less wastes the most valuable thing a learner has: time. Coursera has a trusted library of materials from authoritative sources. LearnVector plans to work with Coursera to bring this trustworthy learning to everyone. I'm grateful to Greg Hart and the entire Coursera team for supporting LearnVector. I look forward to working with our talented team to change how we learn, and accelerate human development.

If semantic search compared every embedding, RAG wouldn't scale.Here's what makes retrieval fast in vector-based RAG.Your documents live as embeddings, so you can search by meaning instead of exact wording.Answering a query means finding the vectors closest to it.The obvious way: compare against every stored vector, return the closest.That is exact nearest neighbour search.↳ Cost: O(N) per query, N = number of stored vectors.Imagine searching millions of documents to answer a single query.It is too slow and does not scale.So, what do you do?𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴Indexing is organising high-dimensional vector embeddings into a data structure that enables fast similarity search.That data structure is Approximate Nearest Neighbour (ANN).↳ Cost: O(log N) per querySince meaning is encoded into vectors, here similarity search = semantic search.For example: Hierarchical Navigable Small World (HNSW)→ It organises embeddings in a multi-layer graph.→ Each layer contains vectors as nodes.→ Every node is connected to its nearest neighbours via edges.Traversing and descending through the layers routes the query to the right neighbourhood cheaply.ANN algorithms trade a small amount of recall accuracy for massive speed gains.Which is why retrieval rarely ends at the vector search: pipelines fetch more candidates, then re-rank.𝗪𝗵𝗲𝗿𝗲 𝗶𝘁 𝗮𝗹𝗹 𝗹𝗶𝘃𝗲𝘀A vector database.It is the storage system that enables fast similarity search at scale and exposes a search API on top of it.This is where indexing arranges embeddings into ANN structures.It holds:→ the embedding vectors→ the original text→ the metadata you filter on→ the ANN indexIndexing is not a one-time build. The structure is maintained on every insert, update and delete.This is what makes fast retrieval possible in vector-based RAG.💾 Save this for your next build➕ Follow for more practical GenAI insights♻ Repost if this helps someone in your networkActivate to view larger image,

Ask a coding agent where something lives and it keyword-greps, so a function sharing one word with your query outranks the one you meant. Graft seeds the lexical matches, then runs personalized PageRank over the call graph the way search ranks the web: a match wired into the code your query touches rises, an isolated namesake sinks. Deterministic, no embeddings. 154 stars, MIT.

After today, plotting in Python will never be the same!!! This github repo is pure gold!!! 100 million points. Rendered in 0.08 seconds. The exact same speed as a chart with 10 thousand points. 🤯 I had to re-read that table three times. Reflex just open-sourced xy, a Python charting library with a Rust core and WebGL2 rendering. In their live-interactivity benchmark, render time stays flat at ~0.08s from 10k all the way to 100M points. That's 34× faster than the next best at 10M, and 177× at 50M. Plotly doesn't finish at 50M. Matplotlib taps out at 100M. At 1 billion points, xy still hands you a working interactive chart in 1.24s. The one that broke my brain: a 10M-point interactive scatter exports to 258 KiB of HTML. Plotly's version is 259 MiB. And it's a drop-in swap "matplotlib.pyplot" for "xy.pyplot" and your code just runs. "pip install xy"

I built a software factory for Claude Code and rebuilt it from scratch. Here's what I learned: My first version tried to automate everything inside one giant workflow. Halfway through implementing a feature, I realized the plan needed changing. Or I'd want to review an architectural decision before continuing. Or fix one small task without waiting hours for the entire pipeline to finish. So I rebuilt Squid around three simple stages instead: 𝟭/ 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 Better plans matter far more than better prompts. And this is where humans still matter most. I start with a rough feature idea. Then I "grill" it with an LLM to uncover: • Missing requirements• Weak assumptions• Architectural gaps• Edge cases The output is: • A polished specification• ADRs• A project glossary• A list of implementation tasks 𝟮/ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 I stopped thinking about this as one coding agent. Instead, it's a conversation between two roles: • Software Engineer• Tester One writes the code. The other tries to break it. They iterate until the task is complete. For larger changes, I let the whole feature run autonomously. For quick fixes, I only execute a single task. The workflow adapts to what I need. 𝟯/ 𝗥𝗲𝘃𝗶𝗲𝘄 This was probably the biggest surprise... Reviews should happen against the pull request diff rather than the entire repository. Once I changed that, review quality improved dramatically. I also merged two roles into one. Instead of having a product manager and an architect, I now have a product architect. Coding agents don't suffer from the communication gaps humans do. Keeping product intent and architectural reasoning together produces much better feedback. After review comes: • CI.• Formatting.• Linting.• Integration tests.• Regression checks. If anything fails, a new implementation task is created automatically. Here's the gist: Everyone talks about agent loops. But loops are only one part of a software factory. The actual value comes from everything around them. This is the architecture I've been gravitating towards while building Squid. Check it out here: https://lnkd.in/dejXAQBc So far, it's producing some of the cleanest AI-generated code I've worked with. P.S. What's been your biggest bottleneck when building with coding agents?

Your agent's memory has no undo button. That's been true since agents got memory, one shared store, mutated in place, and a single wrong write quietly corrupts what the agent believes with nothing to isolate or roll back.Memoria just put Git on the memory itself.Snapshot the memory. Branch a risky change. Merge it only once it checks out. Roll back to any earlier state when it doesn't. The same workflow that made code safe to change, applied to what the agent knows, built on MatrixOne's copy-on-write engine, so branching is zero-copy instead of duplicating the store.It's 409 stars and early, and it leans on running MatrixOne underneath, this is the idea proven, not yet the default stack. But the framing is the part that sticks: agent memory was always treated as a database to write to. It's actually a codebase to version.Repo in the comments.

Latency Optimization Techniques 🚀 Latency Optimization: Making AI Lightning Fast Users hate waiting. If your AI takes 10 seconds to respond, they will abandon your app. Reducing latency is a top priority in production. Here's what actually happens behind the scenes 👇 ✅ Streaming: Sending text back to the user word-by-word (like ChatGPT) instead of waiting for the whole paragraph to finish ✅ Faster Hardware: Using specialized chips (TPUs/Groq) for inference ✅ Optimized Retrievers: Speeding up the Vector DB search query ✅ Parallel Processing: Running API calls and database searches at the same time, not sequentially Example: Instead of waiting 5 seconds to generate a full email draft, Streaming displays the first word in 0.5 seconds. The user reads while the AI is still typing, making it feel instantaneous. Why this matters: Dramatically improves User Experience (UX) Critical for voice-based AI applications where delays are unnatural 💡 Speed is a feature. Optimize your entire pipeline for latency.

Forget 100s of AI eng. videosI handpicked 6 videos that cover the core of it👇If you want a practical path, watch these in order:1️⃣ Python for AI & Agents - Dave Ebbelaar→ Python foundations for real AI workflowshttps://lnkd.in/dXeYSEyZ2️⃣ FastAPI Full Course - Corey Schafer→ Build APIs and ship your AI app to productionhttps://lnkd.in/d7fK3HdN3️⃣ Intro to Large Language Models - Andrej Karpathy→ Understand how LLMs train, reason, and failhttps://lnkd.in/dDTXT-UP4️⃣ RAG Crash Course - Krish Naik→ Connect LLMs with your own datahttps://lnkd.in/dt7uSG265️⃣ AI Agents Course - Marina Wyss→ Task decomposition, tools, and multi-agent flowshttps://lnkd.in/d7x3U46y6️⃣ What Exactly is LLMOps? - Myself (Shirin)→ Trace, debug, evaluate, and monitor agents in productionhttps://lnkd.in/dSgtRB-qHope this helps!

𝟭𝟳𝟴 𝗵𝗼𝘂𝗿𝘀 𝗼𝗳 𝗰𝗹𝗲𝗮𝗻 𝗠𝗮𝗹𝗮𝘆𝗮𝗹𝗮𝗺 𝘀𝗽𝗲𝗲𝗰𝗵. 𝗙𝗿𝗲𝗲. 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝗹𝗼𝘄-𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 𝘀𝘁𝗮𝘆 𝗹𝗼𝘄-𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝘂𝗻𝘁𝗶𝗹 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝗼𝗽𝗲𝗻𝘀 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮.Malayalam has ~38 million speakers and almost no open speech corpora. That's the whole reason its voice models lag behind.So here's Malayalam-Speech-178h:🎙️ 86,799 clips, 48 kHz🧹 Fully cleaned — vocal isolation (BS-RoFormer) → de-reverb → denoise → enhancement⏱️ 178 hours of processed speech🆓 Public on Hugging FaceOne thing up front: this is unlabeled audio. No transcripts. It's built for self-supervised pretraining, voice/speaker modelling, or as raw material for your own labelling pipeline — not for supervised ASR out of the box.I'd rather tell you that now than have you download 123 GB and find out.The expensive part is already done. Source separation and denoising at this scale takes serious GPU time — that's what you're getting for free.from datasets import load_datasetds = load_dataset("psk/malayalam-speech-178h", split="train")🔗 Link in the first comment.If you're working on Malayalam speech — or any Indian language — take it and build something.And if you're sitting on processed audio nobody else has, consider putting it out there. The gap in Indian-language speech data doesn't close by itself.

Generative AI for Healthcare (Part 1): Demystifying Large Language ModelsThis video lecture from Stanford covers- Introductions and Disclosures - Why Is Prompting Hard? - The Three Epochs of Healthcare AI - Tokenization and Embeddings - Transformer Architecture and Self-Attention - Pre-Training and the Evolution of LLMs - Post-Training: Making the Model Helpful and Aligned - The Reasoning Era: Scaling Test-Time Compute Lecture - https://lnkd.in/d7b27wMw

Cost Optimization in GenAI Systems 🚀 Cost Optimization: Stopping the AI Money Burn GenAI is incredibly powerful, but API costs and GPU hosting can bankrupt a project if left unchecked. You must architect for cost-efficiency. Here's what actually happens behind the scenes 👇 ✅ Model Routing: Sending easy questions to cheap models (GPT-3.5/Llama 3 8B) and hard questions to expensive models (GPT-4/Claude 3.5 Sonnet) ✅ Prompt Compression: Removing unnecessary words and whitespace from prompts to save tokens ✅ Semantic Caching: Caching answers that mean the same thing, even if phrased differently Example: User A: "How do I cancel?" User B: "I want to cancel my account." A Semantic Cache recognizes these are the same question and serves the free cached response to User B, saving you API costs. Why this matters: Makes enterprise AI ROI positive Allows you to serve more users on the same budget 💡 The best GenAI engineers are as good at managing budgets as they are at writing code.

Cost Optimization in GenAI Systems 🚀 Cost Optimization: Stopping the AI Money Burn GenAI is incredibly powerful, but API costs and GPU hosting can bankrupt a project if left unchecked. You must architect for cost-efficiency. Here's what actually happens behind the scenes 👇 ✅ Model Routing: Sending easy questions to cheap models (GPT-3.5/Llama 3 8B) and hard questions to expensive models (GPT-4/Claude 3.5 Sonnet) ✅ Prompt Compression: Removing unnecessary words and whitespace from prompts to save tokens ✅ Semantic Caching: Caching answers that mean the same thing, even if phrased differently Example: User A: "How do I cancel?" User B: "I want to cancel my account." A Semantic Cache recognizes these are the same question and serves the free cached response to User B, saving you API costs. Why this matters: Makes enterprise AI ROI positive Allows you to serve more users on the same budget 💡 The best GenAI engineers are as good at managing budgets as they are at writing code.

One month ago, I open-sourced audio.cpp. Yesterday, audio.cpp reached 1,000 GitHub stars and Alibaba Tongyi Lab integrated audio.cpp into the FunASR deployment platform🎉.The long-term vision isn't just an audio AI framework. It's to build a private, portable, high-performance runtime for AI models, with no Python required. At its core, this is about AI accessibility: making advanced models easier to deploy, easier to run privately, and practical on everything from gaming PCs to edge devices.I chose audio AI as the starting point because I believe it is one of the hardest domains. Unlike LLMs or diffusion models, audio models are a diverse collection of architectures, tokenizers, codecs, preprocessors, and pipelines. If we can understand and unify these building blocks, the same ideas can extend much further.In one month:🎉 The project grew from 12 to 44 model families, with 31 contributors.🎉 7 community-contributed model families were added in the two weeks after I made audio.cpp community-friendly.🎉 Engineers from Alibaba and Mistral AI contributed models and improvements to make them more production-ready.🎉 Shared model specifications, reusable components, CLI and server interfaces, streaming support, and experimental pipelines moved the project closer to the original vision of a unified AI runtime.The performance has been exciting—all measured on my gaming GPU. Just a few examples:🚀 VibeVoice inference dropped from around 120 minutes for a 90-minute podcast in Python ---- to just 18 minutes!🚀 Supertonic 3 generates about 10 hours of audiobook in 3 minutes.🚀 Nemotron ASR achieved the same WER as widely used implementations in roughly one-quarter of the time.There is still a long road ahead. Building a truly portable AI runtime opens many research questions beyond model implementations: memory planning, graph optimization, scheduling, quantization, heterogeneous execution, framework architecture, and developer experience. If you're interested in systems research or building the next generation of AI runtimes, I'd love to collaborate.Thank you to everyone who has contributed, tested, reported issues, or simply given the project a star. Here's to the next milestone. 🚀

GPT 6 is dropping soon. Rumor has it that Sam Altman is planning to visit Washington this week to preview the project.⁠Capable of original scientific discovery - solved an 80 year old math problem autonomously⁠Runs for extended periods without supervision⁠Powerful cyber capabilities — likely the model behind the Hugging Face incidentNew framing shifts from benchmarks to "knowledge per dollar"This is not an incremental ChatGPT update. The scale of what's being described is different.THE MODEL WAR JUST ENTERED A NEW PHASE

Google DeepMind unveils Gemini Robotics 2 — a major step toward general-purpose humanoid robots.Gemini Robotics 2 enables full-body control, from walking, crouching, and balancing to precise five-finger object manipulation.The system is powered by three models:• Gemini Robotics 2 — Converts vision and natural language instructions into real-world robot actions.• Gemini Robotics ER 2 — Handles long-horizon planning, tracking hundreds of decisions across multi-minute tasks and coordinating multiple robots.• On-Device 2 — Runs entirely on the robot and adapts to new hardware with fewer than 200 demonstrations collected in just a few hours.A single Gemini Robotics 2 checkpoint successfully controlled both Apptronik Apollo humanoids and the Franka Duo robot, demonstrating strong cross-platform generalization.Current performance still leaves room for improvement:🔹 Whole-body task success: 45.7%–76.3%🔹 Five-finger manipulation: 32% (dustpan use) to 92% (unscrewing a light bulb)The significance isn’t perfect accuracy—it’s a foundation model that can transfer across different robot bodies while combining perception, reasoning, planning, and control in one system.