AI interviews aren't getting harder.They're getting more practical.A few years ago, knowing embeddings and vector databases was enough.Today, interviewers want to know what happens after you deploy.Can your agent remember past conversations?How do you prevent context windows from overflowing?When should you use PostgreSQL instead of a vector database?How do you update millions of embeddings without downtime?How do you isolate memory between users?How do you reduce vector search latency?These aren't trick questions.They're production problems.In this carousel, I cover 15+ interview questions that go beyond definitions.You'll Understand:• Short-term vs long-term memory architecture• Conversation memory management• Relational vs document vs vector databases• Hybrid search with BM25 + Vector Search + RRF• HNSW, IVF, IVF-PQ, and DiskANN• Multi-tenant memory isolation• Embedding versioning and document updates• Retrieval metrics like Recall@K, MRR, and NDCG• Production failure modes and caching strategiesOne thing I've noticed after taking interviews:Strong candidates don't just explain what a technology does.They explain why it was chosen, what trade-offs it introduces, and how it behaves in production.That's what separates someone who has watched tutorials from someone who can build AI systems.If you're preparing for AI Engineer, GenAI Engineer, or LLM Engineer interviews, save this carousel.If you want to clear AI interviews 99% confidently, this Interview Kit is for you.Learn in depth → Practice → Perform → Crack the jobEnroll here: https://lnkd.in/guPzFkTe♻️ Repost if this helped➕ Follow Naresh Edagotti for more real-world AI breakdowns

AI agents are the next leverage layer.But most people still don’t know where to start.Here’s a complete roadmap to go from “curious” to shipping real AI agents:✦ Chapter 1: Understanding AI Agents (the foundation).✦ Chapter 2: Popular AI agent frameworks you should actually know.✦ Chapter 3: Specialized frameworks + emerging protocols.✦ Chapter 4: Step-by-step: Build your first AI agent.✦ Chapter 5: Hosting & deployment (get it live).✦ Chapter 6: Security & compliance (don’t get burned).✦ Chapter 7: Advanced topics + future directions.✦ Chapter 8: Your Journey in AI Agent DevelopmentNo fluff. No hype.Just a structured path from zero → deployed agent.If you’re serious about building AI agents (not just consuming it), this is your starting point.Get FREE access here: https://lnkd.in/dMGZuZAj

I finally decided to pull the trigger and set up a little local Linux box to run my agents on. The reason why I did this is that my MacBook Pro was completely dying under the strain of agents.I got Asus NUC 15 Pro, Ultra 7 255H CPU, 96GB of RAM, 2TB SSD. The whole thing was about $3000, but you can do it way cheaper too. My boy math is that it a Mac Studio could be as much as $10,000, so I'm practically making money.Codex has great Remote support for this set up, much smoother than I thought - I can create new tasks from my Codex mac app or from my phone - all feels pretty similar to how local tasks work. Then I use Tailscale to move any files around too, delightful. I'll leave it next to my router hidden away, no extra clutter around the house.Set up took a couple of hours, it took a bit of back & forth, but agents are pretty good at holding your hand during the setup.Inspired by a Theo YouTube video about moving to Linux, really not as scary as I thought.Sticker art by a wonderful London based artist Natalia Shaloshvili.

🤷 Nico Martin 1w Liquid AI just released LFM2.5-2.6B, and it is wild what a model this compact can do. I built a research agent that runs entirely in the browser and puts its full think, plan, and act loop on display. Give it a question and it: - Creates an explicit action plan - Reasons about the next step - Chooses and calls tools - Inspects each result - Updates the plan and repeats It does not stop after one tool call. The agent keeps looping until every step in the plan is complete. Along the way, it can ask clarifying questions, use local context, delegate focused research to an isolated Wikipedia subagent, and collect sources. Once the plan is done, it turns the evidence into a cited response and downloadable research paper. The incredible part is the size: just 2.6B parameters. That makes LFM2.5 seriously fast on-device with WebGPU, while still being capable enough to plan, reason, use tools, and finish multi-step objectives. The model, agent loop, and inference pipeline all run locally. No inference server, no account, and prompts and outputs stay in the browser. Small models are not just autocomplete. They can be agents at the edge. Try the research agent in your browser: https://lnkd.in/dZwa9PAN Explore the code on GitHub: https://lnkd.in/d4aq-zEE More about the release: https://lnkd.in/dqXb5dvk

Master these LLM questions before your next interview 👇 40 questions, grouped the way a real LLM interview actually moves — definitions first, production last. Save this and work through one topic a day. 1) LLMs: Fundamentals & Comparison • What are Large Language Models (LLMs)? • How are LLMs different from traditional language models? • What are Foundation Models? 2) Tokenization & Embeddings • What is tokenization, and why is it important in LLMs? • What role do embeddings play in LLMs? • How do LLMs handle out-of-vocabulary (OOV) words? 3) Encoder–Decoder & Pretraining Objectives • What are Sequence-to-Sequence (Seq2Seq) models? • What is the difference between an encoder and a decoder? • How do autoregressive models differ from masked models? • What is Next Sentence Prediction (NSP)? 4) Transformer Architecture & Attention • How does the Transformer overcome the challenges of traditional Seq2Seq models? • What are positional encodings in LLMs? • What is attention in Transformer models? • What is Multi-Head Attention? • How are attention scores computed in Transformers? • What is the softmax function, and why is it used in attention? • How is the dot product used in self-attention? 5) Decoding & Sampling Strategies • What is beam search, and how does it differ from greedy decoding? • Explain the concept of temperature in LLM text generation. • What is the difference between top-k sampling and top-p sampling? • How does Adaptive Softmax speed up LLMs? 6) Loss Functions, Gradients & Backpropagation • What is cross-entropy loss, and why is it used in language models? • How are gradients computed with respect to embeddings? • What is the role of the Jacobian matrix in backpropagation? • What is the vanishing gradient problem, and how do Transformers solve it? • What are eigenvalues and eigenvectors, and how are they used in dimensionality reduction? • How is KL divergence used in evaluating LLMs? 7) Fine-Tuning & Parameter-Efficient Training • What are LoRA and QLoRA? • How can catastrophic forgetting be mitigated in LLMs? • How does Parameter-Efficient Fine-Tuning (PEFT) prevent catastrophic forgetting? • What is model distillation, and how is it applied to LLMs? 8) Overfitting & AI Model Types • What is overfitting, and how can it be prevented? • What are Generative and Discriminative models? • What is the difference between Discriminative AI and Generative AI? Want detailed answers to all 40? Every question is fully solved inside the 22 course AI Interview Master Bundle — 1200+ interview questions with answers, company-wise question sets, and beginner-friendly, scenario-first explanations - https://lnkd.in/g9GVkihi

I trained a VAD model from scratch in about a 20-hour A100 GPU run. But first question: what is VAD? Voice Activity Detection asks one simple thing: is anyone speaking right now?𝐖𝐡𝐚𝐭 𝐕𝐀𝐃 𝐃𝐨𝐞𝐬- VAD reads audio frame by frame.- Each frame is labeled as speech or non-speech.- Silence and noise should stay inactive.- Human speech should switch listening on.[This type of post takes a lot of effort to make, so I would appreciate it if you could repost this so that more people can learn about Audio AI fundamentals.]𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬- Classic agents run VAD before ASR, the LLM, and TTS.- VAD gives boundaries: when to listen, stop, and ignore the room.- If VAD is wrong, the rest of the voice stack already feels broken.𝐌𝐨𝐝𝐞𝐫𝐧 𝐒𝐩𝐞𝐞𝐜𝐡-𝐓𝐨-𝐒𝐩𝐞𝐞𝐜𝐡- Newer systems are moving beyond strict turn-taking.- Full-duplex models listen while speaking.- That enables barge-in, interruptions, pauses, and natural timing.- The timing problem becomes turn detection, semantic VAD, and interruption control.𝐅𝐫𝐚𝐦𝐞𝐬 𝐀𝐧𝐝 𝐇𝐨𝐩 𝐒𝐞𝐪𝐮𝐞𝐧𝐜𝐞- The waveform is split into 25 ms frames.- The model advances with a 10 ms hop, so nearby frames overlap.- Speech is continuous, but models need fixed slices.- Overlap keeps context between frames.𝐒𝐜𝐨𝐫𝐞𝐬 𝐓𝐨 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬- Every 10 ms frame gets P(speech), a value between 0 and 1.- Above the start threshold, speech begins.- Below the end threshold for long enough, the segment closes.- Those rules turn probabilities into speech regions.𝐌𝐨𝐝𝐞𝐥 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞- I used a MarbleNet-style network from scratch.- It uses stacked 1D depthwise-separable convolution blocks.- Blocks use residual connections, batch norm, ReLU, and dropout.- A final 1x1 convolution maps features to speech/non-speech logits.𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐒𝐞𝐭𝐮𝐩- Trained in roughly 20 GPU-hours.- The model has about 143k parameters.- The causal receptive field is around 1.7 seconds.- Training used LibriSpeech, MUSAN, and RIRS mixtures.- Checkpoint exported to ONNX.𝐓𝐞𝐬𝐭 𝐑𝐞𝐬𝐮𝐥𝐭𝐬- Clean speech was detected as speech.- Generated silence returned no speech segments.- Speech mixed with white noise still produced a usable region.[Just a quick reminder to repost this post]Paper Sourcehttps://lnkd.in/gMBcPP6mHere is the Github Codehttps://lnkd.in/gZygfWQ8Model weightshttps://lnkd.in/g9Kmrh2eI have also implemented a Speech to text transformer from scratch Here is a Detailed Visual Blog & Videohttps://lnkd.in/gs8bGSwWI Mayank Pratap Singh will be posting more Audio AI implementations and breaking down Audio and LLM inference concepts in future posts, so if you are interested in Audio deep learning and LLM inference optimization, follow me Mayank Pratap Singh for that.

If you are building agents, it's time to revisit the fundamentals: sync vs async. 🔹 𝐖𝐡𝐲 𝐚𝐬𝐲𝐧𝐜 𝐦𝐚𝐭𝐭𝐞𝐫𝐬  LLM API calls take ~2 seconds. → Sync (single worker): User 1 blocks for 2s → Users 2, 3… queue up → 50th user waits 100 seconds. → Async: User 1 hits await → event loop picks up User 2 → all 50 users get responses in ~2 seconds. Async doesn't make one request faster. A 2s call in sync is still 2s in async. It just doesn't block while waiting. 🔹 𝐖𝐡𝐞𝐧 𝐭𝐨 𝐚𝐬𝐲𝐧𝐜 𝐯𝐬 𝐬𝐲𝐧𝐜 I/O bound (waiting on an external service)? Async unblocks you. ✅ API calls (OpenAI, document processing API, etc.) ✅ Databases (PyMongo Async, asyncpg) ✅ Vector stores (Pinecone, Weaviate) ✅ Streaming responses CPU bound? Sync is fine — async won't help. ❌ Local model inference (CPU/GPU busy with matrix math) ❌ JSON parsing, numpy operations ❌ Local document processing (pypdf) For heavy CPU work, use a background job queue instead (e.g. Celery). 🔹 𝐒𝐨𝐦𝐞 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐈 𝐮𝐬𝐞 𝐢𝐧 → Async DB queries: every await db.query() releases control so the loop handles others in the meantime. → Multi-step RAG pipelines (query rewrite → route → search → rerank → generate): each await releases control, the pipeline doesn't block other users. → Streaming with AsyncGenerator: tokens stream as they arrive instead of waiting for the full response. ⚠️ 𝐓𝐡𝐢𝐧𝐠𝐬 𝐭𝐨 𝐰𝐚𝐭𝐜𝐡 𝐨𝐮𝐭 → Agent fan-out: Multiple API calls per request means 100 users can turn into 500+ requests fast. API rate limits hit quickly. Use semaphores to cap concurrency, and retry with exponential backoff. → Missing timeouts: always set one. A stuck call holds resources indefinitely. → Mixed sync/async: I have a FastAPI service that hits PostgreSQL on almost every request. 𝘴𝘺𝘯𝘤 drivers in 𝘢𝘴𝘺𝘯𝘤 endpoint blocks the event loop on every DB call, reducing concurrency and throughput. Where has async bitten you in production?

Harvard’s Senior AI Engineer path is now open to everyone for free.And Prof. Vijay Janapa Reddi put the full ML Systems (CS249r) class on GitHub.That’s a real shortcut if you want to learn what top teams do in the real world.The big pieces are all there:1. Architecture2. Data pipelines3. Production4. MLOps5. Edge AI6. PrivacyThis is the part of Big Tech that usually stays behind closed doors.Now it’s out in the open.Study it.Save it.Use it.Link: https://lnkd.in/d7-3TvEm

𝗧𝗼𝗱𝗮𝘆 𝘄𝗲 𝗮𝗿𝗲 𝗿𝗲𝗹𝗲𝗮𝘀𝗶𝗻𝗴 𝗟𝗲𝘁𝘁𝘂𝗰𝗲𝗗𝗲𝘁𝗲𝗰𝘁 𝘃𝟮: 𝗦𝗢𝗧𝗔 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗰𝗼𝗱𝗲, 𝘁𝗼𝗼𝗹 𝗼𝘂𝘁𝗽𝘂𝘁, 𝗮𝗻𝗱 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 — 𝗶𝗻 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 [**vLLM**](https://www.linkedin.com/company/vllm-project?trk=public_post-text) 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗥𝗼𝘂𝘁𝗲𝗿 𝘁𝗲𝗮𝗺.  In agentic workflows, hallucinations are rarely just text. Agents ground their answers in 𝘀𝗼𝘂𝗿𝗰𝗲 𝗰𝗼𝗱𝗲, 𝘁𝗼𝗼𝗹 𝗼𝘂𝘁𝗽𝘂𝘁, 𝗺𝗮𝗿𝗸𝗱𝗼𝘄𝗻, 𝘁𝗮𝗯𝗹𝗲𝘀. Existing hallucination datasets and detectors were built for document QA and 𝗻𝗼𝗻𝗲 𝘄𝗲𝗿𝗲 𝗯𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝘁𝗵𝗶𝘀: on code-agent answers prior detectors reach 𝟬.𝟭𝟳 𝘀𝗽𝗮𝗻-𝗙𝟭, and even 550B-class LLM judges reach at most 𝟬.𝟮𝟮.  So we built the 𝗳𝗶𝗿𝘀𝘁 𝘀𝗽𝗮𝗻-𝗹𝗲𝘃𝗲𝗹 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗮𝗻𝗱 𝗺𝗼𝗱𝗲𝗹𝘀 for hallucination detection over code, tool output, and structured documents, with multilingual coverage built in.  The results:  🥇 𝗦𝗢𝗧𝗔 𝗼𝗻 𝗼𝘂𝗿 𝗻𝗲𝘄 𝗰𝗼𝗱𝗲/𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸: 𝟬.𝟲𝟬 span-F1 on code-agent answers, 𝟬.𝟲𝟴𝟵 on the full test set — \~3× the best prior detector 🌍 𝗦𝗢𝗧𝗔 𝗼𝗻 𝗣𝘀𝗶𝗹𝗼𝗤𝗔: best reported English IoU (𝟬.𝟳𝟮𝟰), with 14-language coverage 💪 𝘀𝘁𝗿𝗼𝗻𝗴 𝗼𝗻 𝗥𝗔𝗚𝗧𝗿𝘂𝘁𝗵: 𝟴𝟭.𝟴 example-F1 from a 2B model, so specializing on code gives strong general RAG performance  The detectors take the request, the context, and the answer, and return the 𝗲𝘅𝗮𝗰𝘁 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗲𝗱 𝘀𝗽𝗮𝗻𝘀, located to the character, 𝘁𝘆𝗽𝗲𝗱 (contradiction / fabricated reference / unsupported addition × 13 subcategories), each with an explanation.  We are releasing:  🧪 a 𝘂𝗻𝗶𝗳𝗶𝗲𝗱 𝘀𝗽𝗮𝗻-𝗹𝗲𝘃𝗲𝗹 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸: 𝟳𝟰,𝟮𝟴𝟱 newly constructed examples across SWE-bench coding-agent traces, developer tool output, and structured documents (papers, READMEs, Wikipedia) — 𝟭𝟰𝟱𝗞+ training examples with RAGTruth and 14-language PsiloQA folded in, all with exact character labels 🤖 𝗹𝗲𝘁𝘁𝘂𝗰𝗲𝗱𝗲𝗰𝘁-𝘃𝟮-𝗾𝘄𝗲𝗻-𝟮𝗯 — SOTA generative detector, typed spans + explanations in a single pass, 𝟯𝟮𝗞-𝘁𝗼𝗸𝗲𝗻 context ⚡ 𝗹𝗲𝘁𝘁𝘂𝗰𝗲𝗱𝗲𝗰𝘁-𝘃𝟮-𝗺𝗺𝗯𝗲𝗿𝘁-𝗯𝗮𝘀𝗲 — 𝟯𝟬𝟳𝗠 multilingual encoder for high-throughput pipelines 🏷️ a 𝘁𝗮𝘅𝗼𝗻𝗼𝗺𝘆 𝘁𝘆𝗽𝗶𝗻𝗴 𝗵𝗲𝗮𝗱 (𝟬.𝟴𝟮 category accuracy on gold spans) that types any binary span detector 💻 the full 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 construction, training, and evaluation pipeline  This comes from our new paper and collaboration with [**Bowei He**](https://hk.linkedin.com/in/bowei-he-8a9450199?trk=public_post-text), [**Xunzhuo Liu**](https://sg.linkedin.com/in/bitliu?trk=public_post-text) and [**Huamin Chen**](https://www.linkedin.com/in/huaminchen?trk=public_post-text) from the [**vLLM**](https://www.linkedin.com/company/vllm-project?trk=public_post-text) 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗥𝗼𝘂𝘁𝗲𝗿 team.  The models are integrated into [**vLLM**](https://www.linkedin.com/company/vllm-project?trk=public_post-text) SR with span-level verification inside the serving stack. More on that in the next days. 🚀  Everything is open source under permissive licenses.  Paper / HF links in the comments. If you like it, a star means a lot to us ⭐

𝟗𝟎% 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐋𝐋𝐌 𝐭𝐨𝐤𝐞𝐧𝐬 𝐚𝐫𝐞 𝐮𝐬𝐞𝐥𝐞𝐬𝐬 𝐧𝐨𝐢𝐬𝐞.If you run AI agents or build LLM pipelines, you know the pain: verbose server logs, raw JSON dumps, and bloated code context quickly inflate API costs.To solve this, 𝐒𝐞𝐧𝐢𝐨𝐫 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐚𝐭 𝐍𝐞𝐭𝐟𝐥𝐢𝐱 just open-sourced #Headroom—a local middleware proxy that cuts LLM token consumption by 𝟔𝟎% 𝐭𝐨 𝟗𝟓% without changing model outputs.𝐇𝐨𝐰 𝐢𝐭 𝐖𝐨𝐫𝐤𝐬Headroom acts as an intelligent intermediary between your application and your LLM provider (OpenAI, Anthropic, etc.):𝟏. 𝐂𝐨𝐧𝐭𝐞𝐧𝐭-𝐀𝐰𝐚𝐫𝐞 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧: Uses specialized engines to strip structural bloat from JSON, compress AST code bases, and summarize raw logs.𝟐. 𝐑𝐞𝐯𝐞𝐫𝐬𝐢𝐛𝐥𝐞 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 (𝐂𝐂𝐑): Stores full context locally. If an LLM needs exact raw data, it can query Headroom back on demand—keeping it virtually lossless.𝟑. 𝐋𝐨𝐜𝐚𝐥 & 𝐏𝐫𝐢𝐯𝐚𝐜𝐲-𝐅𝐢𝐫𝐬𝐭: Runs entirely on your machine; no sensitive codebase data leaves your network for compression.𝟒. 𝐙𝐞𝐫𝐨-𝐂𝐨𝐝𝐞 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: Works as a middleware wrapper around tools like Claude Code, Cursor, Copilot, and Aider.𝐐𝐮𝐢𝐜𝐤 𝐒𝐞𝐭𝐮𝐩:𝗽𝗶𝗽 𝗶𝗻𝘀𝘁𝗮𝗹𝗹 𝗵𝗲𝗮𝗱𝗿𝗼𝗼𝗺-𝗮𝗶𝗵𝗲𝗮𝗱𝗿𝗼𝗼𝗺 𝘄𝗿𝗮𝗽 𝗰𝗹𝗮𝘂𝗱𝗲That’s literally it. No app code rewrites required.🔗 𝐆𝐢𝐭𝐇𝐮𝐛 𝐑𝐞𝐩𝐨𝐬𝐢𝐭𝐨𝐫𝐲: https://lnkd.in/dwsi9HmW

AirLLM is a Python library that lets 70B parameter language models run on a single 4GB GPU, without quantization, distillation, or pruning. The problem it's solving is access. Large open-source models keep getting released, but running them normally requires enough GPU memory to hold the entire model at once, which puts most of them out of reach for anyone without high-end or multiple GPUs. AirLLM's approach: during inference, the original model is first decomposed and saved layer-wise. That's how it avoids needing the full model resident in memory at once. For MoE models specifically, it goes further and streams one expert at a time rather than a whole layer, since a token typically doesn't need every expert to run. It works with almost any popular model, you just pass a Hugging Face repo ID, and it works the same way regardless of model size or family, Llama, Qwen, DeepSeek, Mistral, Phi, Gemma, ChatGLM, Baichuan, InternLM, and others are all supported. There's also an optional compression feature, block-wise quantization that delivers up to 3x faster inference with almost ignorable accuracy loss. It's Apache 2.0 licensed, has 25.5k stars, and has been actively maintained, with new model support added on an ongoing basis. Here's the GitHub Repo: https://lnkd.in/dV7FDsWG

350k+ agent skills shipped in starting three months.None of them had proper governance. Same happened with MCPDevelopers share MCP servers via git repos and zip files.No versioning. No scanning. No signing.It's the container supply chain chaos all over again.Except these artifacts take actions on your behalf.Jozu MCP Registry solves this with OCI-compatible packaging:→ Implements MCP registry spec natively→ VS Code, Cursor, Claude Desktop connect directly→ Every server scanned across 9 vulnerability classes→ Cryptographic signatures via Cosign→ Policy gating before production→ Tamper-evident audit trailFor developers = curated catalog of pre-vetted tools.Not a security ticket queue.Runtime governance through Agent Guard:Agent Guard sits between your agent and the host machine, running in an isolated microVM (Apple Virtualization framework on Silicon).→ Agent Guard AI Gateway evaluates every tool call from inside the microVM→ Tool-level policy enforcement per invocation→ Workspace shared via virtiofs (live bidirectional mount, no sync step)→ Network traffic goes through user-space stack, never touches host interfaces directly→ Per-session credentials minted at boot, destroyed on exit→ Human-in-the-loop for high-risk actions→ Works on air-gapped environmentsThe binary embeds everything: Linux kernel (2.5MB), initrd (10MB), AI gateway (40MB), Debian rootfs (~500MB).Boot takes ~1 second after first run.No Docker, QEMU, or network fetch required.Built on KitOps (CNCF) and ModelPack spec.Works with Harbor, ECR, GCR, Artifactory, Docker Hub.Same workflow you have today.Just point your IDE at a different registry endpoint.https://lnkd.in/g-CQV-KB

This little GitHub trick is honestly wild. You can open almost any repo in an AI workspace just by editing the URL. Take any repo link. Add "explain" right before "github" in the address. So github.com/owner/repo becomes https://lnkd.in/dE3MtdPf. Hit enter, and the whole codebase opens in an AI workspace. From there you can → Explore the file structure without cloning anything → Ask questions about how the code actually works → Understand the architecture in plain English → Generate onboarding style reports for a new project → Get answers that point back to the exact files they came from That last part is the one that matters. File-cited answers mean you are not just trusting a summary. You can jump straight to the source and verify it. I keep coming back to how useful this is for open source. The hardest part of contributing to a new project is understanding it before you touch anything. This turns hours of digging into a few minutes of asking. Works on GitHub and GitLab, and it is free to try. If you explore other people's code often, save this one. Follow me for more amazing AI, Coding & Web Dev insights. #AI #GitHub #OpenSource #Coding #DevTools #ai #agenticai #ahmedislam #ahmedislamai #agents #githubtricks #tricks

AI models are becoming a commodity.AI systems are becoming the moat.McKinsey found that 88% of organizations now use AI.BCG found that only 5% are capturing value at scale.MIT’s research was even sharper: 95% of enterprise GenAI pilots show no measurable P&L impact.The gap is not intelligence.It is architecture.Most companies are buying Ferrari engines and installing them in horse carriages.Open almost any serious AI product.You will not find a model working alone.You will find a system:Retrieval.Memory.Tools.Permissions.Workflows.Monitoring.Evaluation.Governance.The model is the visible 10%.The system is where the value lives.LLMs generate language.RAG grounds that language in enterprise knowledge.Agents connect that intelligence to tools and workflows.Agentic AI coordinates multiple agents toward goals.Every layer changes the company.LLM → RAG: now you need data governance.RAG → Agents: now you need execution permissions.Agents → Agentic AI: now you need decision rights.That is why so many programs stall.They buy the model.They skip the trust architecture.Gartner predicts more than 40% of agentic AI projects will be canceled by the end of 2027.Gartner even has a name for the hype: agent washing.Calling something autonomous does not make it governed.The next AI advantage will not come from who has the best model.Everyone will have frontier intelligence.The advantage will come from who can safely connect intelligence to work.Language → Grounding → Execution → Orchestration.Intelligence was never the bottleneck.Trust architecture is.Your CEO is promising autonomous agents.Your teams are still trying to get RAG through compliance.That gap is the real AI strategy.From Pilots to Platforms.

ScrapeGraphAI is known for pointing an LLM at a web page and getting structured JSON back. Convenient, until you scrape those same pages every day and pay the model each time. Describe the fields you want once and its CodeGenerator writes you a small Python parser instead, runs it, checks the result, and fixes it until it holds. From there, extraction costs nothing. 28,9k stars, MIT. #WebScraping #AIEngineering #DataEngineering #Python #OpenSource

We’ve been receiving so many messages about how useful our open source "AI Interview Hub" has been, esp. the 10 AI system design questions!!Do check it out if you’re currently interviewing, and share it with anyone else who might find it helpful.The repository currently covers - AI Product Managers - AI Forward Deployed Engineers- AI Strategists- AI EngineersEach role has different interview rounds, expectations, and common questions, so we’ve created a clear roadmap for each one. Since many of these roles are still new, we wanted to make this information easier to access.We’ve included 10 real world system design use cases, with code for each one and guidance on how to approach the question. This is also where we see most candidates struggle, so I hope this section is especially useful.For each of the system design questions we have a PM version and an engineer version so that you can focus on the same problem from different angles as well ⛳ Customer Support Agent ⛳ Enterprise Knowledge Assistant ⛳ Text to SQL Analytics ⛳ Invoice and Expense Decisioning ⛳ Underwriting Document Decisioning ⛳ Security and Compliance Agent ⛳ Coding Agent ⛳ Voice Support Agent ⛳ SDR Sales Agent ⛳ Clinical Scribe Check out my GitHub repository, awesome-generative-ai-guide, or feel free to request a link in your email from the link below in the comments

RAG Beginner's Roadmap Here is the roadmap - Python language - Generative AI basics - LLM basics - Prompting techniques - LLM frameworks (LangChain or Llama Index) - Chunking - Data extraction - Embeddings - Vector databases - RAG basics - RAG implementation from scratch - RAG implementation with LangChain or Llama Index - Agent basics - Agentic RAG - Advanced RAG techniques - Build RAG Apps - RAG Evaluation & Monitoring - Deploy RAG Apps LLM Engineer Toolkit - https://lnkd.in/gVs_aqVp

Developers spend so much time writing prompts instead of building & shipping software. This consumes significant engineering bandwidth and slows down the process. The core idea of Loop Engineering is to eliminate developer intervention altogether through agentic automation. In the software development context, this includes tasks such as: - Verifying the working of unit tests. - Ensuring the coverage is above a threshold. - Running the linters. - Executing stress tests and guaranteeing no performance impact. To remove developer intervention, the developer would state the success criteria and the agentic system would verify whether the criteria is met after making the code changes. It would continue until the criteria is met. Once developer intervention is eliminated, the agent becomes autonomous and works until the task is done. We can define Loop Engineering as - The art of designing autonomous systems that work towards a goal without human prompts. As Boris Cherny, the creator of Claude Code said - “I don’t write prompts any more. I write loops that self-prompt Claude Code”. The below diagram illustrates how the Agent interacts with the LLM and drives a goal to completion. Here is my complete hands-on walkthrough on loop engineering: https://lnkd.in/dNBJR9_C If you like to know more in-depth on Agentic loop engineering and best practices, this is my hands-on guide: https://lnkd.in/gFs_hXD2 Image credits: Animesh Gaitonde

𝟮 𝗺𝗶𝗻𝘂𝘁𝗲𝘀 𝗼𝗳 𝗺𝘂𝘀𝗶𝗰 𝗼𝗻 𝗮 𝗠𝗮𝗰 𝗖𝗣𝗨 𝗶𝗻 𝟲 𝘀𝗲𝗰𝗼𝗻𝗱𝘀. 𝗡𝗼 𝗚𝗣𝗨.Stability AI released Stable Audio 3 — and the interesting part isn't the audio quality.It's where it runs.━━━━━━━━━━━━━━The family:→ Small-Music / Small-SFX — 433M, CPU, up to 2 min→ Medium — 1.4B, CUDA, up to 6m 20s, peaks at ~6.5 GB VRAM→ Large — 2.7B, API and enterprise only, not openThree of four ship open weights.━━━━━━━━━━━━━━Why it's fast:→ A new autoencoder (SAME) compresses stereo 44.1 kHz audio 4096× into 256-dim latents at ~10.76 Hz→ Variable-length diffusion — a 20s clip costs 20s of compute, not the max sequence length→ 8-step ping-pong sampling after adversarial post-training→ No classifier-free guidance at inference — CFG is baked in during distillation, so you pay one forward pass per step instead of twoMedium generates 380 seconds of audio in 1.31s on an H200.━━━━━━━━━━━━━━The part most people will skip:It edits. Inpaint one region, inpaint multiple disconnected regions, continue a track, restyle an existing recording — everything outside the mask stays untouched.Plus LoRA training in-repo. Stackable, adjustable at runtime.That's a production engine, not a demo.━━━━━━━━━━━━━━The licensing, stated plainly:→ Trained on fully licensed data→ Community License: you own your outputs, commercialise freely→ Above $1M revenue: Enterprise License, with legal indemnification→ Code is MIT, weights are notIn a market where the big music generators are in court, "we can tell you where the training data came from" is a feature.━━━━━━━━━━━━━━Honest limits:→ Instrumental only. No vocals.→ Small models are domain-locked — music or SFX, pick one→ Prompt prefixes are mandatory on most variants or output degrades━━━━━━━━━━━━━━Where this lands: on-device game audio, foley copilots, offline sonic branding, editors that regenerate only the selected bar.Not one giant cloud model. Small specialised audio models fine-tuned inside the product.We build exactly this — on-prem and edge inference, LoRA-adapted audio and voice models, deployed inside your infrastructure.

The number one TTS model on the blind leaderboards right now isn't ElevenLabs. It's an open-weight project you can run on your own server for free. I moved my whole voice stack over to Fish Audio last month, and the reason has almost nothing to do with how it sounds. Most voice AI is a meter running under your product. Every call your agent makes, every line your app speaks, every character costs money, and it never stops. The day you stop paying, your app goes silent. Fish Audio’s S2 Pro is available as an open-weight model, so once my volume got real I pulled it onto my own servers and my per-character bill went to zero. Here's what that unlocks if you build: 1️⃣ 𝗩𝗼𝗹𝘂𝗺𝗲 𝘀𝘁𝗼𝗽𝘀 𝗯𝗲𝗶𝗻𝗴 𝘀𝗰𝗮𝗿𝘆 A voice agent doing thousands of calls a day is a rounding error on your own GPU, and a serious invoice on someone else's API. 2️⃣ 𝗬𝗼𝘂 𝗰𝗮𝗻 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗲 An open weight model can be fine tuned on your own data. Closed tools won't let you near the weights. 3️⃣ 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗰𝗮𝗻'𝘁 𝗯𝗿𝗲𝗮𝗸 𝘆𝗼𝘂 When a company reprices next year, your product keeps running instead of dying overnight. The voice itself holds up too. I cloned mine from a 15 second clip, ran scripts across 83 languages from one model, and controlled emotion by typing tags straight into the text instead of dragging sliders. It does everything I used ElevenLabs for. ElevenLabs still wins on ecosystem, the bigger library, the dubbing, the polish for people who want a no-code studio. If that's you, stay there. But if you're shipping voice into your own apps, owning the layer beats renting it every time. Over to you: if you're building with AI voice, are you renting your voice layer or owning it? #fishaudiopartner