London, New York, LA and San Francisco offer the WORST value on earth 💸Valerii Emelianov mapped 100 cities on cost of living vs quality of life using Numbeo data:▫️ Migration magnets rank worst: high costs, expensive housing, services that stop matching expectations▫️ Porto, Valencia and Prague have held the top of the value ranking for 7 years, and all three keep drifting right as costs climb▫️ Europe has the widest spread on the planet, from Switzerland to the Balkans▫️ Sun Belt and Pacific Coast cities are climbing fast on domestic migration▫️ Some cheap winners are pockets of comfort for elites and tourists rather than the median residentQuality of life here means purchasing power, safety, healthcare, housing, traffic, pollution and climate

I've been teaching in some form since I was 14. The biggest lesson it's taught me is: 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆.Which is actually great news right now. There's no shortage of AI courses out there. Different formats, different depths, different styles. You get to pick what fits how you learn.🤔 𝗕𝘂𝘁 that raises a 𝗕𝗜𝗚 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: with so many options out there, including ones built with AI in a weekend, why choose my course?Three reasons, and none of them are the curriculum. 1️⃣ 𝗧𝗵𝗲 𝘀𝘁𝗼𝗿𝗶𝗲𝘀. What AI can't generate is real lived stories! A decade of bringing AI products at Google, Microsoft to millions of developers, or leading AI transformation for some of the biggest companies in the world. You're not paying for slides. You're paying for the war stories behind them.2️⃣ 𝗧𝗵𝗲 𝗰𝗿𝗲𝗱𝗶𝗯𝗶𝗹𝗶𝘁𝘆. Experience is scar tissue. I've been in the boardroom and in the trenches leading AI strategy to change management for companies like Chevron, UBS, Zurich Insurance, Barclays, etc getting thousands of developers to actually adopt AI.3️⃣ 𝗧𝗵𝗲 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆. My courses pull in everyone from senior leaders to individual contributors, all in the same room, actually talking to each other. Connecting right hires to right hiring managers. And that third one matters the most. In the AI age, 𝘁𝗵𝗲 𝗼𝗻𝗲 𝘁𝗵𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝘀𝘁𝗮𝘆𝘀 𝗲𝗻𝘁𝗶𝗿𝗲𝗹𝘆 𝘆𝗼𝘂𝗿𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱. Not the certificate! That's what I'm building, one cohort at a time.👍 Want to be part of the community? I would love to have you for the next cohort starting in a few days with 30% discount EARLYBIRD: https://lnkd.in/gZAw3Y94 👇 Photo: See what some of the students had to say about the course. hashtag#AIEducation hashtag#LinkedInLearning hashtag#TechnicalStorytelling hashtag#AITransformation hashtag#CommunityBuilding hashtag#CareerGrowth hashtag#LeadershipLessons

I've been teaching in some form since I was 14. The biggest lesson it's taught me is: 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆.Which is actually great news right now. There's no shortage of AI courses out there. Different formats, different depths, different styles. You get to pick what fits how you learn.🤔 𝗕𝘂𝘁 that raises a 𝗕𝗜𝗚 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: with so many options out there, including ones built with AI in a weekend, why choose my course?Three reasons, and none of them are the curriculum. 1️⃣ 𝗧𝗵𝗲 𝘀𝘁𝗼𝗿𝗶𝗲𝘀. What AI can't generate is real lived stories! A decade of bringing AI products at Google, Microsoft to millions of developers, or leading AI transformation for some of the biggest companies in the world. You're not paying for slides. You're paying for the war stories behind them.2️⃣ 𝗧𝗵𝗲 𝗰𝗿𝗲𝗱𝗶𝗯𝗶𝗹𝗶𝘁𝘆. Experience is scar tissue. I've been in the boardroom and in the trenches leading AI strategy to change management for companies like Chevron, UBS, Zurich Insurance, Barclays, etc getting thousands of developers to actually adopt AI.3️⃣ 𝗧𝗵𝗲 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆. My courses pull in everyone from senior leaders to individual contributors, all in the same room, actually talking to each other. Connecting right hires to right hiring managers. And that third one matters the most. In the AI age, 𝘁𝗵𝗲 𝗼𝗻𝗲 𝘁𝗵𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝘀𝘁𝗮𝘆𝘀 𝗲𝗻𝘁𝗶𝗿𝗲𝗹𝘆 𝘆𝗼𝘂𝗿𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱. Not the certificate! That's what I'm building, one cohort at a time.👍 Want to be part of the community? I would love to have you for the next cohort starting in a few days with 30% discount EARLYBIRD: https://lnkd.in/gZAw3Y94 👇 Photo: See what some of the students had to say about the course. hashtag#AIEducation hashtag#LinkedInLearning hashtag#TechnicalStorytelling hashtag#AITransformation hashtag#CommunityBuilding hashtag#CareerGrowth hashtag#LeadershipLessons

RedNote’s Dots Studio team has fully open-sourced dots.tts, a 2B-parameter, fully continuous, end-to-end autoregressive text-to-speech foundation model.Unlike mainstream TTS systems that generate discrete acoustic tokens, dots.tts models speech directly in a continuous latent space, bringing together high-fidelity zero-shot voice cloning, multilingual speech synthesis, expressive generation, and real-time streaming.🌟 Key Highlights:🌊 Fully continuous autoregressive architecturedots.tts removes discrete acoustic tokens from the speech-generation pipeline. It combines a 48 kHz AudioVAE, a semantic encoder, an LLM, and an autoregressive flow-matching acoustic head to generate speech one continuous latent patch at a time.🧠 Improved long-range consistencyFull-history conditioning allows the acoustic head to use the complete generated prefix, while reward-free self-corrective post-training exposes the model to its own inference-time errors. Together, these designs help reduce drift and improve robustness during long autoregressive generation.⚡ Ultra-low-latency dual streamingCFG-aware MeanFlow distillation reduces acoustic generation to only 2–4 function evaluations, achieving first-packet latencies of 85 ms in output-streaming mode and 54 ms in dual-streaming mode, making the model suitable for real-time conversational applications.🌍 Strong multilingual voice cloning and expressivenessTrained on 1.5 million hours of multilingual speech, dots.tts has been evaluated across 24 languages and demonstrates strong multilingual and cross-lingual voice cloning, as well as expressive speech generation.🏆 Open-source state-of-the-art performancedots.tts achieves strong results on major TTS benchmarks. The release includes pretrained, post-trained SOAR, and MeanFlow-distilled checkpoints, together with training, inference, and fine-tuning code under the Apache 2.0 license.🚀 Get started:👉 GitHub: https://lnkd.in/ecSN2-t8👉 Hugging Face Models: https://lnkd.in/euBvdAcv👉 Online Demo: https://lnkd.in/egcVMBfw👉 Paper: https://lnkd.in/eqHWx-gx

Someone just open-sourced the entire AWS cloud in 13 MB. Every local AWS emulator before this needed gigabytes of RAM. Cold starts took 30 seconds just to test one function.   A team just open-sourced Floci. It's a single Go binary that runs the entire cloud in memory.   The whole footprint is 13 MiB. The average Chrome tab uses 200x more than that.   It boots 45 services in under a second. Point the standard SDK at localhost and existing scripts work untouched.   What runs locally with zero dependencies:   > S3, Lambda, DynamoDB, SQS > IAM, KMS, Cognito, STS > Step Functions, EventBridge, CloudFormation > API Gateway, Kinesis, Secrets Manager   No daemon, no Python runtime, no Java VM.   Pre-built binaries ship for Linux, macOS, and Windows. Tests finish before LocalStack Pro pulls its Docker image. The repo is 100% open-source with no paid tier.

The 100% free stack to launch your product in 2026 👇🏽0. Run your idea through the "grill-me" prompt on ChatGPT to make sure you are building something valuable (prompt in comments).1. Start the UI with Google AI Studio. Free and the simplest tool in town.2. Export the code to GitHub.3. Clone in your local computer.3. Make any changes through Google's Anti-gravity or OpenCode - both have generous tiers and free model access.4. Connect with a backend using Supabase. Free.5. Push back on GitHub.6. Use Vercel to deploy in production, and share the URL with your users.All cloud services - GCP, Azure, AWS have generous free tiers like Vercel.7. Do not forget to add PostHog for analytics. (Just signup on Posthog, and ask Antigravity to integrate PostHog with your app, no need for any manual work).In 2026 you are not limited by your tech skills, you are limited by your ability to judge - which problems are worth solving.Here are a few resources to learn this whole process:1. Become an AI Builder: https://lnkd.in/gFTimcUX2. Learn analytics: https://lnkd.in/g2VaJnuA3. Tech 101 for PMs: https://lnkd.in/gNp8vN4g

As a mom to a 3-year-old, I recently started researching schools in Charlotte — and quickly found myself jumping between school websites, maps, academic data, and lots of browser tabs. That sparked a question: 𝗖𝗼𝘂𝗹𝗱 𝗜 𝘂𝘀𝗲 𝗔𝗜 𝘁𝗼 𝗺𝗮𝗸𝗲 𝘁𝗵𝗶𝘀 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝘀𝗶𝗺𝗽𝗹𝗲𝗿 for parents? So I built 𝗖𝗵𝗮𝗿𝗹𝗼𝘁𝘁𝗲 𝗦𝗰𝗵𝗼𝗼𝗹 𝗦𝗲𝗮𝗿𝗰𝗵. 🎓 What started as a simple school finder evolved into three parts: 📍 𝗙𝗶𝗻𝗱 𝗦𝗰𝗵𝗼𝗼𝗹𝘀 — discover nearby schools on an interactive map and filter by level and type. 📊 𝗖𝗼𝗺𝗽𝗮𝗿𝗲 𝗦𝗰𝗵𝗼𝗼𝗹𝘀 — compare schools using official NC performance, growth, math, and reading data. 🤖 𝗔𝗜 𝗦𝗰𝗵𝗼𝗼𝗹 𝗔𝗱𝘃𝗶𝘀𝗼𝗿 — ask questions in plain English and get answers grounded in the same school data. For now, the app is intentionally restricted to Charlotte-Mecklenburg Schools (CMS) and relies on official CMS and NC DPI data wherever possible. What makes this project special to me is that it started with a real question in my own life — how do I make a thoughtful school decision for my daughter? — and became an opportunity to explore how AI can make real-world information easier to navigate. The goal was to build a practical application that helps parents explore Charlotte-area schools through a streamlined interface instead of manually piecing information together. 🔗 Try it here: https://lnkd.in/eFz-23e3 I’d love feedback, especially from Charlotte parents: What else would you want to know when researching a school for your child? A big shout out to Aishwarya Srinivasan, Arvind Narayanamurthy and The Gen Academy for demystifying so many of the buzzwords around AI. hashtag#AI hashtag#AgenticAI hashtag#GenAI hashtag#EdTech hashtag#Python hashtag#Streamlit hashtag#CharlotteNC hashtag#BuildInPublic Activate to view larger image,

Stop wasting hours searching for AI agent codeIf you want to build AI agents fast,you do not need another blog post or course.You need high quality repos you can clone, study,and turn into working products.So here is a curated list of 25 GitHub reposcovering agents, LLMs, RAG, ML, RL, and real use cases.All in one place.

Anthropic won't like this open-source repo.It is going to cost LLM providers a lot of money.Every CI run of an AI app today sends real requests to providers like OpenAI or Anthropic.Like any other LLM call, this too gets billed at actual API rates. So for teams with high commit volumes, this accumulates into a meaningful chunk of API spend.One common hack devs use is that instead of invoking the LLM API, the test calls a fake local server that speaks the same API and returns a dummy response.The catch is that the dummy response is a copy of what the provider returned on the day it was saved, and providers keep adding fields and changing types.So the tests keep passing against a schema that's no longer valid, while the real integration breaks in production.A smart approach is now actually implemented in CopilotKit's recently open-sourced aimock project.Every day, the repo's own CI sends a handful of requests to the real API and the same requests to the fake server, then compares both against the official client library's type definitions.Those are the only real API calls in the whole setup, and they run on the repo's own keys, not in anyone else's CI.A single team can push hundreds of commits a day, and thousands of teams are already doing that with coding agents.All of those runs stay offline, because one repo checks against the real API on everyone's behalf.When a check fails, a coding agent updates aimock's built-in response schema, the full test suite has to pass, and a patch version ships to npm.By simply upgrading the package, the corrected schema gets reflected in every project using it.The capability is not just limited to a single provider.The same server works for Claude, OpenAI, Gemini, Bedrock, Azure, Ollama, plus MCP tools, A2A agents, AG-UI event streams, vector DBs like Pinecone and Qdrant, and search, speech, image, and video endpoints.

We often hear from AWS users: "Ok, we get it, you provide all the building blocks, but how do I combine all of these to build my agent control plane for all the agents, tools, and skills my organization is running, including identity, policies, discoverability, and observability?"AWS has all the primitives we need to build a robust agent-first control plane. This post walks through the high-level architecture.

watermarks-remover installs as an agent skill and strips AI provenance marks across Claude, Gemini/SynthID and OpenAI. Layer A removes invisible Unicode and bidi characters. Layer B rewrites text to break statistical watermarks. File cleaning covers C2PA, EXIF and XMP in PNG, JPEG, SVG, PDF, DOCX, HTML and Markdown. Core scripts are Python 3.10+ stdlib. Soft binding and audio watermarks stay out of scope. 6.6k stars, MIT.#AIGovernance #C2PA #ContentProvenance #AIAgents

Wait what... NVIDIA just brought Object Oriented Programming to AI agents 🤯Your agent is just a Python class. No tool schemas. No prompt templates. No separate workflow graph.It's called NVIDIA Object Oriented Agents. 100% Open Source, model agnostic.Every framework today makes you define the same agent in four places. The prompt sits in a string. The tool sits in a JSON schema. The state sits in a dict. The orchestration sits in a graph you wire up somewhere else. Change one and the other three quietly go stale.NOOA maps all four onto things Python already has.Fields are the agent's state. Methods are its capabilities. Docstrings are the prompts. Type annotations are the output contract.Then comes the part that makes this actually work: a method with an empty body becomes an LLM-driven agentic loop. Write a real body and it stays deterministic Python. You choose method by method what the model decides and what your code decides, inside the same class.When the model does act, it writes Python in a Jupyter-style REPL with access to self. Your methods are already the callable interface, so there are no tool schemas to maintain.Everything you would normally wire up around that call is already handled: • Typed inputs and outputs with auto-retry when the model returns wrong shape• Live objects passed by reference instead of serialized copies• Every LLM call, code execution, and method invocation traced by default• Runs on Anthropic, OpenAI, Ollama, or vLLM through LiteLLM• SWE-bench Verified and Terminal-Bench 2.0 results published in the paperAgents execute generated code here, so NVIDIA is direct about running them sandboxed.

Everyone wants to become an AI Engineer… until they see what the job actually looks like.→ Debugging API failures at 2 AM→ Fighting token limits and latency→ Switching models to save cost→ Fixing broken RAG pipelines→ Tuning vector databases→ Monitoring logs, retries, edge cases→ Making sure the system doesn’t break in productionAI Engineering is not about “using tools”It’s about building systems that actually work in the real world.The gap between demo and production is where real engineers are made.If you’re only writing prompts, you’re just getting started.If you want to clear AI interviews 99% confidently, this Interview Kit is for youLearn in depth → Practice → Perform → Crack the jobEnroll here: https://lnkd.in/guPzFkTe

5K stars in less than 24 hours… and growing. 🤯Well, that didn’t take long.AI labs are starting to add more provenance signals to AI-generated content.Open source responded immediately:watermarks-remover.A new repo designed to strip multiple types of AI provenance marks across ecosystems including Claude, Gemini and OpenAI.It can tackle:→ Invisible Unicode characters→ C2PA / EXIF / XMP metadata→ Document properties→ Statistical text watermarks through rewritingAnd it works across PNG, JPEG, PDF, DOCX, HTML, Markdown and more. The cat-and-mouse game between AI provenance and watermark removal has officially started.And open source moves ridiculously fast.

Yesterday we released LFM2.5-VL-3Band with it, a 𝗴𝘂𝗶𝗱𝗲 𝗼𝗻 𝗵𝗼𝘄 𝘁𝗼 𝘂𝘀𝗲 𝗶𝘁𝘀 𝘃𝗶𝘀𝗶𝗼𝗻 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀:In this guide, you'll learn how to do:• Single-image prompt• Multi-image prompt• OCR• Document layout annotations• Object detection and grounding• Tool calling

AI agents do not browse. They do not wander. They do not get lost.That changes how merchants should think about their data.For twenty years we optimized for human behavior: page views, scroll depth, cart abandonment, session data. In agent-led commerce, that entire signal layer disappears. An agent evaluates your product attributes, your pricing logic, your return policy and your fulfillment SLAs, and then decides whether you make the shortlist. There is no brand voice or site design, just structure and clarity.The practical implication for merchants is that data modernization stops being a cost-control project and starts being a revenue question. If your data is inconsistent, you are not losing efficiency. You are losing consideration.

“Freelancers are unreliable.”“They’ll disappear after taking the project.”“The quality won’t be good.”“A full-time employee is always better.”These are some of the biggest myths I hear from startup founders about hiring freelancers. But let’s look at it differently.Myth #1: Freelancers are expensive.You don’t pay for 12 months of employment when you need 2 weeks of expertise.Myth #2: Freelancers aren’t committed.A good freelancer’s reputation is their business. Your success can become their next referral.Myth #3: Quality is unpredictable.So is hiring a full-time employee. The solution is a proper portfolio, references, trial project and clear scope.Myth #4: Freelancers can’t understand your business.Give them context, objectives and ownership. Many freelancers work with multiple businesses and bring experience from all of them.Myth #5: You need to hire a team to scale.Sometimes you need a network of specialists, not a payroll full of generalists.The biggest advantage?Flexibility.Need a designer for 10 days? Hire one.Need a developer for 3 months? Hire one.Need a growth expert for a specific problem? Hire one.No long-term commitment.No unnecessary headcount.No waiting months to build a team.𝐓𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐢𝐬𝐧’𝐭 𝐧𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐢𝐥𝐲:𝐄𝐦𝐩𝐥𝐨𝐲𝐞𝐞𝐬 𝐎𝐑 𝐟𝐫𝐞𝐞𝐥𝐚𝐧𝐜𝐞𝐫𝐬.𝐈𝐭’𝐬:𝐄𝐦𝐩𝐥𝐨𝐲𝐞𝐞𝐬 + 𝐟𝐫𝐞𝐞𝐥𝐚𝐧𝐜𝐞𝐫𝐬 + 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 + 𝐀𝐈.The smartest founders will know when to use each one. What’s the biggest myth about freelancers you’ve heard?

800ms of voice-agent latency can come from distance, not the model.A voice agent can have a fast LLM and still feel painfully slow.Here’s where the hidden latency and cost often come from:1️⃣ Distance → LatencyUser in Mumbai→ Audio travels to the US→ STT processes it→ LLM responds→ TTS generates audio→ Audio travels backThat network round trip can add significant latency before the model even starts working.2️⃣ Queuing → DelaysTraffic spikes→ Provider capacity gets saturated→ Requests wait in queue→ Agent pauses mid-conversation→ Caller experiences silence3️⃣ Repeated Calls → Higher CostOne conversation→ STT on every turn→ LLM on every turn→ TTS on every turn→ Repeated responses generated againA 16-turn call can create roughly 48 model calls.4️⃣ PII → Data ExposureCaller shares sensitive information→ Raw audio/transcript leaves your system→ Third-party infrastructure processes it→ Different jurisdiction + retention policies5️⃣ Observability → Blind SpotsUser says: “The call felt slow.”But where?→ STT?→ Routing?→ LLM?→ TTS?→ Network?Without step-level visibility, debugging becomes guesswork.6️⃣ Provider Lock-In → Slower InnovationBetter model becomes available→ Current app depends on one provider SDK→ Switching requires refactoring→ Evaluation gets postponed→ You stay with the existing modelThe bigger lesson:Voice AI performance is not only a model problem.It’s an execution problem.A production-ready voice architecture needs:→ Regional routing→ Smart caching→ Efficient model calls→ PII controls→ Step-level observability→ Provider flexibilityThe model generates the intelligence.The execution layer determines how efficiently users experience it.Want to explore the execution layer behind this approach?→ Check out SLNG : https://www.slng.ai/Save this if you’re building production voice agents.➕ Follow Naresh Edagotti for practical AI Engineering interview questions and real-world scenarios.

I deleted 62.5% of a 120B-parameter model. Production couldn't tell the difference. At reach.jobs we score resumes against job postings with OpenAI's gpt-oss-120b. The full model is 61 GiB of weights: that's RTX 6000 Pro territory, a $10,000 card, before you even talk about context. So I built the world's first REAP-pruned gpt-oss-120b: 80 of 128 experts deleted per layer, calibrated on our real traffic. It now runs on a single $1,000 32 GB GPU with the full 128k context, in stock vLLM, on NVIDIA and Intel alike. On a frozen replay of our production workload it scores inside the full model's own run-to-run consistency range, at 100% valid JSON. 10x cheaper hardware, same answers. And it beats gpt-oss-20b (the model you'd otherwise run at this size) on every metric we measured. One lesson worth stealing: calibrate on prompts PLUS the model's own generations. Prompt-only calibration silently deletes the experts that write your output format, and the model fails by format collapse, not gradual decay. Weights, code (Apache 2.0), and the full write-up: https://lnkd.in/gfYqYcWx https://lnkd.in/gzK8yHkm https://lnkd.in/gWXyCWqF If your workload is narrow, your MoE is mostly dead weight. Measure it. #LLM #OpenSource #GPU #MachineLearning #Inference

GenAI Interviewer Question Series Interview: Two retrieved documents contain different answers to the same question. One is from last year, while the other was updated yesterday. Question: How would you design the retrieval and ranking pipeline to identify the authoritative and most recent information? Explanation: Metadata Filtering: Store document version, effective date, source, department, and authority level as metadata during ingestion. Retrieval: Use hybrid search to retrieve semantically relevant documents, then apply metadata filters where appropriate. Authority Ranking: Assign higher ranking to trusted sources, such as official policies, over drafts or user-generated documents. Recency Ranking: Boost documents with newer effective dates, not simply upload timestamps. Reranking: Use a cross-encoder/reranker combining relevance, authority, version, and freshness signals. Conflict Detection: If two high-confidence sources still conflict, trigger a conflict-resolution step rather than blindly selecting one. Generation: Instruct the LLM to prioritize the authoritative, latest effective document and provide citations. Evaluation: Measure retrieval accuracy, freshness, source correctness, and conflict-resolution accuracy. _________________________________________________________________________ Preparing for an ML or AI interview or Looking for Transition in AI? Check out the resource below, it covers key concepts with 1000 interview question Answers, Roadmap, Projects and practical topics to help you prepare with confidence. 🔗 Link: https://lnkd.in/dez6Ji7E