Taalas Chip: Infer-No ⚡ Silicon-Llama. .
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Plus: 10 Claude Code Tricks Most Still Don’t Know, Shared by Core Claude Insiders
What if AI didn’t just run on chips… but was literally baked into them? And what if repeating your prompt twice could 5x–10x model accuracy?
What’s on FIRE 🔥
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AI INSIGHTS
🧠 The Chip That Hardwires Intelligence: 17,000 Tokens per Second
Taalas just launched HC1, a custom “Hardcore” ASIC that reportedly hits up to 17,000 tokens per second for inference. They embed Meta’s Llama 3.1-8B directly into the silicon. The model lives inside the chip.
→ Forbes says that’s about 10× faster than Cerebras in certain setups and potentially up to 100× faster than GPUs. It literally hardwires the model into the chip.
That’s how they get insane speed and very low cost per token. Reported numbers include:
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~14,357 tokens/sec for long responses
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Up to 17,000 tokens/sec per user
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~$0.75 per 1M tokens (claimed)
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12–15kW per rack vs 120–600kW GPU racks
And they just raised $169M to push this forward. It’s built for one target model. If you want a different model, you likely need a different chip. That means data centers might need multiple model-specific racks.
There’s also quality tradeoffs. The current “Silicon Llama” uses aggressive quantization (mixed 3-bit and 6-bit weights). That boosts efficiency but can reduce accuracy. Future HC2 chips aim for 4-bit floating point formats to close that gap.
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AI SOURCES FROM AI FIRE
1. 10 Claude Code Tricks Most Still Don’t Know, Shared by Core Claude Insiders. Here is exactly how I use Claude Code to handle complex projects, automate boring tasks, and learn new skills fast
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Most people use ChatGPT as-is. But very few take the next step: building their own Custom GPT that fits their workflow, brand voice, or business. You’ll see:
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TODAY IN AI
AI HIGHLIGHTS
📈 Anthropic scaled revenue 10× after $1B, way faster than OpenAI’s 3.4×. It may overtake OpenAI by mid-2026. So Sam & Dario aren’t holding hands anytime soon😁
🎮 Microsoft named Asha Sharma as new Gaming CEO after Phil Spencer’s exit. AI will reshape gaming & monetization but vows not to flood Xbox with “soulless AI slop.”
🌍 70+ countries signed the India AI Impact Summit’s “Delhi Declaration”. But the White House rejected it outright, stating the U.S. “totally rejects” global AI governance.
🚀 Nvidia is nearing a $30B equity stake in OpenAI, replacing its $100B chip supply pact. The round could value OpenAI at $830B. SoftBank and Amazon may join.
🔥 Young Indians are powering ChatGPT. Nearly 50% of usage comes from 18-24-year-olds, Codex demand is 3× the global median. India now has 100M+ weekly users.
💰 Big AI Fundraising: Sequoia is leading a record $1B European seed round for Ineffable Intelligence, founded by ex-DeepMind star David Silver, who aims to build “superhuman intelligence.”
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AI BREAKTHROUGH
🔬 Prompt Repetition Improves Non-Reasoning LLMs
Google researchers just published a paper showing that simply repeating your prompt twice can massively boost LLM performance. In some search-style tasks, accuracy jumped from 21% to 97%, without enabling reasoning:
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Prompt repetition is the core trick
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Works especially for non-reasoning model settings
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Helps when key instructions or questions appear late in the prompt
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Gives the model a “second pass” with full context awareness
LLMs read left to right. Early words get interpreted before the model sees later clarifications. When you paste the same prompt again, the second copy is processed with full knowledge from the first pass. In Google’s tests:
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One benchmark improved from 21.33% to 97.33% accuracy
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Across 7 models and 7 benchmarks, repetition beat the normal prompt in 47 out of 70 cases
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It never performed worse in a statistically meaningful way
This suggests many LLM mistakes are “reading order” issues, not knowledge gaps. The fix isn’t always bigger models or deeper reasoning, sometimes it’s just cleaner context timing.
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