How to start creating impactful websites using Vibe Coding
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DINOv3 and the quiet death of task-specific vision pretraining
I swapped DINOv3 into a defect-detection pipeline that used to run on a fine-tuned ResNet. It took an afternoon, not a month.
computer-visionself-supervised-learningdinov3Segment Anything 2 for video: what the demo doesn't show you
SAM 2's video propagation is genuinely good. It's also not the drop-in replacement for a tracking pipeline that the demo videos imply.
computer-visionsegmentationvideoLong context windows solve a different problem than the one you have
Every model release brags about a bigger context window. After actually building against a few of them, I think most teams reaching for 200K tokens have a retrieval problem, not a context problem.
llmlong-contextevaluationRunning a 70B model on a single 24GB card, and what you give up to get there
AWQ and GGUF quantization get talked about like a free lunch. It's a good deal, but it's not free — here's what actually degrades.
llmquantizationlocal-inferenceThe RAG pipeline that worked in the demo and fell apart in week three
Chunking strategy, embedding drift, and the eval gap nobody warns you about until your retrieval quality quietly degrades in production.
ai-engineeringragproductionMost "agents" I've tested fail at the boring part, not the reasoning part
Everyone benchmarks agents on how well they plan. The failures I actually hit in production were almost always tool-call formatting and error recovery, not reasoning quality.
ai-engineeringagentstool-usePrompt caching cut our LLM bill by 60%, and it wasn't the optimization I expected to matter most
We spent weeks optimizing model choice and prompt length before someone pointed out we were re-sending the same 4,000-token system prompt on every single call.
ai-engineeringcost-optimizationprompt-cachingWhat AlphaFold3 actually changed for people who aren't DeepMind
The headline is "predicts protein-ligand complexes now." The part that matters for a working biologist is what you can and can't trust the output for.
biotechmlprotein-structureDiffusion models can generate novel molecules. Making them synthesizable is the part that's still hard
Generative chemistry demos are full of beautiful, valid, entirely novel molecules. A lot of them, a chemist told me, would take a PhD student two years to actually make.
biotechmldrug-discovery