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15 reels match your search.
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Why your RAG pipeline forgets the middle
A 40-second teardown of positional bias: models retrieve the head and tail of context and quietly drop the middle. Fix it with reranking, not bigger windows.
Nadia Okafor · 22s
- 53% matchPopular starting point
Video models are becoming world simulators
Object permanence, gravity, occlusion — emergent physics inside a generative video model, tested with adversarial prompts.
Kenji Aoyama · 27s
- 53% matchPopular starting point
The agent handoff protocol nobody agreed on
Four competing standards for passing state between agents, benchmarked on the same task. One of them silently drops tool errors.
Miles Trent · 23s
- 53% matchPopular starting point
Fine-tuning is back (for narrow tasks)
When a 3B tuned model beats a frontier model on your one task, at 1/60th the cost. The decision tree for tune vs prompt.
Nadia Okafor · 20s
- 53% matchPopular starting point
Sandboxing code interpreters properly
Network egress, syscall filtering, and time limits. A checklist for letting a model run code without letting it run your infra.
Dr. Ilse Rahman · 22s
- 53% matchPopular starting point
On-device robotics policies under 100MB
Distilling a vision-language-action model down to something that runs on the arm's own controller, at 40Hz.
Bruno Kessler · 26s
- 53% matchPopular starting point
Analog compute for attention
An in-memory compute prototype doing matrix multiply in the analog domain — 20x energy savings, with an accuracy asterisk.
Priya Sundaram · 29s
- 53% matchPopular starting point
Agents that write their own tools
Watch an agent hit a missing API, generate a typed client for it, test it, and keep going. The whole run takes 90 seconds.
Miles Trent · 26s
- 53% matchPopular starting point
One prompt injection, three compromised agents
A poisoned webpage cascades through a multi-agent crew. The lesson: treat every tool output as untrusted input, always.
Dr. Ilse Rahman · 19s
- 53% matchPopular starting point
Diffusion transformers, explained with paint
Noise schedules visualized as pigment dissolving in water, then reversed. The clearest 30 seconds on DiT you'll watch today.
Kenji Aoyama · 30s
- 53% matchPopular starting point
The memory wall is the real bottleneck
FLOPs are cheap, bandwidth isn't. A whiteboard walk through HBM economics and why inference clusters look nothing like training ones.
Priya Sundaram · 28s
- 53% matchPopular starting point
Teaching a quadruped to fall gracefully
Reward shaping for recovery instead of avoidance. The robot stops fearing the ground and gets 4x more resilient.
Bruno Kessler · 24s
- 53% matchPopular starting point
Eval-driven development for LLM features
Stop shipping vibes. Build a 40-case golden set before the prompt, and let regressions fail your CI like any other test.
Sam Ortega · 21s
- 53% matchPopular starting point
Speculative decoding on a laptop
A 0.5B draft model feeding a 8B verifier. Same output distribution, 2.4x faster tokens, zero cloud spend.
Wen Li · 18s
- 53% matchPopular starting point
Retrieval is a product problem
Users don't ask questions, they gesture at them. How query rewriting driven by session context beat a fancier embedding model.
Amara Bell · 25s