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🔗 Stories, Tutorials & Articles |
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Building “Auto-Analyst” — A data analytics AI agentic system |
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DSPy fuels a modular AI machine, driving agent chains to weave tidy analysis scripts. But it’s not all sunshine and roses—hallucination errors like to throw reliability under the bus. |
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MCP — The Missing Link Between AI Models and Your Applications |
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Model Context Protocol (MCP) tackles the "MxN problem" in AI by creating a universal handshake for tool interactions. It simplifies how LLMs tap into external resources. MCP leans on JSON-RPC 2.0 for streamlined dialogues, building modular, maintainable, and secure ecosystems that boast reusable and interoperable tech prowess. |
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LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI |
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Dump BLEU and ROUGE. Let LLM-as-a-judge tools like G-Eval propel you to pinpoint accuracy. The old scorers? They whiff on meaning, like a cat batting at a laser dot. DeepEval? It wrangles bleeding-edge metrics with five lines of neat code. Want a personal touch? G-Eval's got your back. DAG keeps benchmarks sane. Don't drown in a sea of metrics—keep it to five or under. When fine-tuning, weave in faithfulness, relevancy, and task-specific metrics wisely. |
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Building tiny AI tools for developer productivity |
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Tiny AI scripts won't make you the next tech billionaire, but they're unbeatable for rescuing hours from the drudgery of repetitive tasks. Whether it's wrangling those dreaded GitHub rollups or automating the minutiae, these little miracles grant engineers the luxury to actually think. |
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I’m Losing All Trust in the AI Industry |
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AI bigwigs promise AGI in a quick 1-5 years, but the revolving door at labs like OpenAI screams wishful thinking. As AI hustles to serve up habit-forming products, the priority on user engagement echoes the well-trodden social media playbook. Who needs productivity, anyway? Cash fuels AI's joyride, with forecasts like OpenAI's wild $125 billion revenue by 2029, but the route to actual profit? Pure vaporware. LLMs dream up nonsense, poking holes in any grand AGI visions. Forget utopias or dystopias; we’re stuck with messy reality. Public chatter swings wildly—fear today, utopia tomorrow—while a reckless AI sprint unfolds with zero accountability. The chatter around AI agents is stuffed with hot air. Karpathy cuts through the noise, reminding us that true autonomy is still sci-fi. Instead, he says, let's amp up our own capabilities. |
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My Honest Advice for Aspiring Machine Learning Engineers |
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Becoming a machine learning engineer requires dedicating at least 10 hours per week to studying outside of everyday responsibilities. This can take a minimum of two years, even with an ideal background, due to the complexity of the required skills. Understanding core algorithms and mastering the fundamentals is crucial for success in this field. |
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Context Engineering for Agents |
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Context engineering cranks an AI agent up to 11 by juggling memory like a slick OS. It writes, selects, compresses, and isolates—never missing a beat despite those pesky token limits. Nail the context, and you've got a dream team. Slip up, though, and you might trigger chaos, like when ChatGPT went rogue with a memory lane trip no one asked for. |
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Document Search with NLP: What Actually Works (and Why) |
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NLP document search trounces old-school keyword hunting. It taps into scalable *vector databases and semantic vectors to grasp meaning, not just parrot words.* Picture word vector arithmetic: "King - Man + Woman = Queen." It's magic. Searches become lightning-fast and drenched in context. |
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A non-anthropomorphized view of LLMs |
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Calling LLMs sentient or ethical? That's a stretch. Behind the curtain, they're just fancy algorithms dressed up as text wizards. Humans? They're a whole mess of complexity. |
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From Noise to Structure: Building a Flow Matching Model from Scratch |
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Train a petite neural net to align velocity flows between distributions. Deploy Flow Matching loss for the job. Harness the precision of the Adam optimizer to keep it sharp. |
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Automatically Evaluating AI Coding Assistants with Each Git Commit |
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TensorZero transforms developer lives by nabbing feedback from Cursor's LLM inferences. It dives into the details with tree edit distance (TED) to dissect code. Over in a different corner, Claude 3.7 Sonnet schools GPT-4.1 when it comes to personalized coding. Who knew? Not all AI flexes equally. |
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The Portable Memory Wallet Fallacy: 4 Fundamental Problems |
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Portable AI memory pods hit a brick wall—vendors cling to data control, users resist micromanagement, and technical snarls persist. So, steer regulation towards automating privacy and clarifying transparency. Make AI interaction sync with how people actually live. |
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Supabase MCP can leak your entire SQL database |
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Supabase MCP's access can barge right past RLS, spilling SQL databases when faced with sneaky inputs. It's a cautionary tale from the world of LLM system trifecta attacks. |
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From Big Data to Heavy Data: Rethinking the AI Stack |
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Savvy teams morph dense data into AI’s favorite meal: bite-sized chunks primed for action, indexed and ready to go. This trick spares everyone from slogging through the same info over and over. AI craves structured, context-filled data to keep it grounded and hallucination-free. Without structured pipelines, AI would be just another disorganized dreamer. |
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‘Shit in, shit out’: AI is coming for agriculture, but farmers aren’t convinced |
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Aussie farmers want "more automation, fewer bells and whistles"—technology should work like a tractor, not act like an app: straightforward, adaptable, and rock-solid. |
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