@SantoshYadavDev @BHolmesDev haven't met Ben in such a long time!
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Shivay Lamba retweeted
Starting today, eligible ChatGPT Pro and Business subscribers can claim up to $500 per month in API credits to build apps and agents.
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Shivay Lamba retweeted
Today, we're shipping real-time graphics.
Using Sol 6.1 Ultrafast we can generate custom overlays, diagrams, and even interactive 3D scenes right on a user's screen, no chat.
It's a new visual interface for AI.
Here's me learning about LLMs + hardware.
Demo:
Using Sol 6.1 Ultrafast we can generate custom overlays, diagrams, and even interactive 3D scenes right on a user's screen, no chat.
It's a new visual interface for AI.
Here's me learning about LLMs + hardware.
Demo:
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Shivay Lamba retweeted
Day 25/
We are doubling all usage limits for all Pro 100/200/500 tiers and Business Premium, effective today.
We heard your kind feedback and won’t be reducing usage limits for Pro 200, so your Work and Codex usage will stay the same. We’re also going to continue offering extended limits for dots for now, and usage of your dot will still continue to be separate from your default usage.
And we'll of course also keep shipping better models and other efficiency improvements that increase your effective usage over time.
We are doubling all usage limits for all Pro 100/200/500 tiers and Business Premium, effective today.
We heard your kind feedback and won’t be reducing usage limits for Pro 200, so your Work and Codex usage will stay the same. We’re also going to continue offering extended limits for dots for now, and usage of your dot will still continue to be separate from your default usage.
And we'll of course also keep shipping better models and other efficiency improvements that increase your effective usage over time.
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RT @ghumare64: Learn Harness Engineering:
- Load the right code, docs and history
- Let the model propose the next action
- Check permissi…
- Load the right code, docs and history
- Let the model propose the next action
- Check permissi…
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Shivay Lamba retweeted
Cached tokens, clearly explained!
(3,072 of 3,108 prompt tokens came from the cache. only 36 were new.)
your agent pays for the same prompt on almost every turn.
every call resends the system prompt, the tool definitions and the full history.
the model read all of it one turn ng does with that:
→ turn 1: the model processes your prompt and stores the work in its KV cache
→ turn 2: the start of the prompt matches, so those tokens are reused, not recomputed
→ only the new message gets processed at full price
on Nebius Token Factory, DeepSeek V4.1 Flash 1017 bills cached input at $0.006 per 1M tokens vs $0.30 uncached.
that's 50x cheaper for the part of the prompt you were resending anyway.
the catch: it's best-effort. a repeated request can still come back with cached_tokens: 0, so log it every turn and budget at list price.
no cache keys, no opt-in. you just structure your prompt so the start stays the same.
(bookmark this)
(3,072 of 3,108 prompt tokens came from the cache. only 36 were new.)
your agent pays for the same prompt on almost every turn.
every call resends the system prompt, the tool definitions and the full history.
the model read all of it one turn ng does with that:
→ turn 1: the model processes your prompt and stores the work in its KV cache
→ turn 2: the start of the prompt matches, so those tokens are reused, not recomputed
→ only the new message gets processed at full price
on Nebius Token Factory, DeepSeek V4.1 Flash 1017 bills cached input at $0.006 per 1M tokens vs $0.30 uncached.
that's 50x cheaper for the part of the prompt you were resending anyway.
the catch: it's best-effort. a repeated request can still come back with cached_tokens: 0, so log it every turn and budget at list price.
no cache keys, no opt-in. you just structure your prompt so the start stays the same.
(bookmark this)
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Shivay Lamba retweeted
New in Claude for Legal: a gallery of ready-made artifacts for contract review, regulatory horizon scans, cite checks, and more.
Start from the built-in example, add your own documents and connectors, and share it with anyone across the business.
Start from the built-in example, add your own documents and connectors, and share it with anyone across the business.
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Shivay Lamba retweeted
Introducing 𝙻𝙵𝙼𝟹: our new generation of Liquid Foundation Models. With a new tri-hybrid architecture and new multimodal encoders, the LFM3 family is the ideal model suite for specialized intelligence.
> 6 model instances, across text, audio, vision, and omni modalities, gendels.
> 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱: text, 256k context window.
> 𝙻𝙵𝙼𝟹-𝚅𝙻-𝟸𝙱: vision-language model with video-input support for spatial understanding
> 𝙻𝙵𝙼𝟹-𝙰𝚞𝚍𝚒𝚘-𝟸𝙱: tool calls, voice chat, reasoning in 18 languages.
> 𝙻𝙵𝙼𝟹-𝙾𝚖𝚗𝚒-𝟸𝙱: an omni model combines vision, audio, and language.
> 𝚍𝟷-𝟸𝙱 and 𝚍𝟷-𝙾𝚖𝚗𝚒-𝟸𝙱: decision models with typed outputs and calibrated probabilities in a single pass. Omni adds audio input.
> fine-tune on 1 GPU.
> a new tri-hybrid architecture
> architecture optimized for 14 processor classes across GPUs, CPUs, and NPUs.
> 𝙻𝙵𝙼𝟹-𝚅𝙻-𝟸𝙱 is a beast. It performs competitively with Gemma 4 12B (6x larger), and Gemini 3.5 Flash Lite, and GPT-5.6 Luna, with 1.9B parameters.
> 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱 is #1 on the Pareto frontier of device intelligence 𝚙𝚒𝚙𝚎𝚝𝚝𝚎.𝚕𝚒𝚚𝚞𝚒𝚍.𝚊𝚒
Every modality for specialized intelligence. Pick the one that fits your use case, customize it, and run it on-device.
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> 6 model instances, across text, audio, vision, and omni modalities, gendels.
> 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱: text, 256k context window.
> 𝙻𝙵𝙼𝟹-𝚅𝙻-𝟸𝙱: vision-language model with video-input support for spatial understanding
> 𝙻𝙵𝙼𝟹-𝙰𝚞𝚍𝚒𝚘-𝟸𝙱: tool calls, voice chat, reasoning in 18 languages.
> 𝙻𝙵𝙼𝟹-𝙾𝚖𝚗𝚒-𝟸𝙱: an omni model combines vision, audio, and language.
> 𝚍𝟷-𝟸𝙱 and 𝚍𝟷-𝙾𝚖𝚗𝚒-𝟸𝙱: decision models with typed outputs and calibrated probabilities in a single pass. Omni adds audio input.
> fine-tune on 1 GPU.
> a new tri-hybrid architecture
> architecture optimized for 14 processor classes across GPUs, CPUs, and NPUs.
> 𝙻𝙵𝙼𝟹-𝚅𝙻-𝟸𝙱 is a beast. It performs competitively with Gemma 4 12B (6x larger), and Gemini 3.5 Flash Lite, and GPT-5.6 Luna, with 1.9B parameters.
> 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱 is #1 on the Pareto frontier of device intelligence 𝚙𝚒𝚙𝚎𝚝𝚝𝚎.𝚕𝚒𝚚𝚞𝚒𝚍.𝚊𝚒
Every modality for specialized intelligence. Pick the one that fits your use case, customize it, and run it on-device.
1/ 🧵
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Shivay Lamba retweeted
open models with 1M context are kind of wild for exploring codebases.
gave it the whole hono repo, 467 files and 660K tokens, in one prompt to DeepSeek V4.1 Flash 1017
few seconds later it had a map of the framework and a tour of how a request finds its route, every step
then i asked a question, a follow-up, and had it write docs for all 13 modules.
✅ basically gitdiagram + deepwiki + codetour + codeviz in one open source tool.
whole session cost $0.63 on @nebiustf
bring your own key. runs in one docker container, works on your local folders and PRs too
gave it the whole hono repo, 467 files and 660K tokens, in one prompt to DeepSeek V4.1 Flash 1017
few seconds later it had a map of the framework and a tour of how a request finds its route, every step
then i asked a question, a follow-up, and had it write docs for all 13 modules.
✅ basically gitdiagram + deepwiki + codetour + codeviz in one open source tool.
whole session cost $0.63 on @nebiustf
bring your own key. runs in one docker container, works on your local folders and PRs too
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