Thanks, glad it landed. For TTFT, the first thing I check is prompt length and whether the prefix is stable enough to hit prompt caching. Most wins come from trimming the system prompt and keeping volatile content at the end, not from model-level changes.
AI
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Clarification on AI Model Prefill and Decode Compute Phases
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Small correction: prefill is compute-bound, decode is the memory-bandwidth-bound phase. But the KV cache point is spot on. It grows linearly with context, and every decode step has to read the whole thing.
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Techniques to Improve AI Sequence Decoding Efficiency
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Yes, and it is linear in sequence length, so the pain shows up fast. This is why GQA, sliding windows, and quantized caches exist. They all attack the same problem from different angles: keep the cache small so decode stays fast.
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Latency Impact of System Prompt Length on AI Model Response
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Yes, and the gap is larger than most expect. A 4K system prompt can add hundreds of ms to TTFT before the model says anything. Streaming hides decode latency but does nothing for prefill. Trimming the system prompt usually beats any decode-side optimization people try first.
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Luma Labs Releases New Image Generation API for Developers
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The numbers:
→ #1 on Human Preference Elo
→ Top 3 in Image Arena
→ ~31 sec/image at half the price of competitors Built with Hollywood cinematographers. Production-ready. Available now in Python & JS SDKs. https://
lumalabs.ai/api Find this and hundreds more AI tools at -
Luma Labs Releases Uni-1.1 Multimodal Transformer API
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🚨 @LumaLabsAI just dropped the Uni-1.1 API—and it's already #1 on Human Preference Elo.
— Futurepedia – Learn to Leverage AI (@futurepedia_io) 7 mai 2026
This isn't just another image gen API. It's a unified transformer that reasons across text + image tokens in a single sequence (not bolted-together models).
What you can do: 🧵 👇️… pic.twitter.com/MQSyM3NfTX@LumaLabsAI just dropped the Uni-1.1 API—and it's already #1 on Human Preference Elo. This isn't just another image gen API. It's a unified transformer that reasons across text + image tokens in a single sequence (not bolted-together models). What you can do:
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Prompt Caching and Context Trimming in AI Models
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Exactly! Tool outputs are usually long and stick around in context, so the cache grows fast. Prompt caching helps the prefill side, but decode still has to read all of it on every token. Trimming what goes back into context matters more than people realize.
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CEO of largest US public hospital ready to replace radiologists with AI
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CEO of America’s largest public hospital system says he’s ready to replace radiologists with #AI
by Marty Stempniak @RadiologyBiz Learn more: https://
bit.ly/4w81dbJ #MedTech #Healthcare #HealthTech #Tech #TechForGood -
Every ML conference since 2019 features at least three subquadratic attention papers
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every ML conference I've been to since 2019 has had no fewer than three papers proposing new techniques for subquadratic attention
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New sub-quadratic attention technique makes long-context LLMs 10x cheaper
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"Introducing a breakthrough new technique for sub-quadratic attention, making long-context LLMs 10x cheaper without sacrificing performance" Me: