@AIatMeta
's Llama Impact hackathon in London is going full swing! Stop by our table with any questions you have and grab swag. As always, we're offering increased rate limits, and can't wait to see what you build on Groq! Good luck to all of the hackers. cc @cerebral_valley
STARTUPS
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Groq Sponsors Meta’s Llama Impact Hackathon in London
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How to Launch an Advertising Agency Using AI
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Voici comment lancer une agence de publicité facilement avec l’IA Et grâce à cette méthode, absolument TOUT LE MONDE peut y parvenir facilement ! Voici comment : [ Ajoutez en signet et RT pour ne pas perdre ! ]
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Anthropic as OpenAI’s Key Competitor Driving Innovation
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Anthropic is the most important competitor we have to OpenAI. They keep pushing them to invent better and especially more efficient models.
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$100B AI Fund Expansion Signals Urgent Infrastructure Demand
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The expansion of a $100B #AI fund. @RodrigoLiang says interest in startups shows “urgent demand for scalable AI infrastructure,” & that Nvidia “can’t keep up with demand.” @nmasc_ of @theinformation writes all about it in a great article below.
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01.ai Trains #6 World Model for $3M with $0.14/M Token Inference
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01.ai trained the #6 model in the world for $3M pre-train cost. And the inference price is $0.14/million tokens! tomshardware.com/tech-indust…
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01.ai Trains GPT-4 Competitor with 95% Fewer Resources
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Chinese startup 01 .ai trains competitive LLM using 95% fewer resources through innovative engineering optimization. 01 .ai trained a GPT-4 competitor using just 2,000 GPUs and $3M, while achieving competitive performance. Through innovative engineering and optimization techniques, they achieved what OpenAI did with $80-100M, demonstrating remarkable cost efficiency in LLM training. → Training Resource Optimization at 01 .ai Using only 2,000 GPUs versus OpenAI's estimated 10,000+ GPUs for GPT-3. The company achieved competitive performance despite severe hardware constraints due to US regulations. → Cost Efficiency Breakthrough $3M total training cost compared to OpenAI's $80-100M for GPT-4. Model ranked sixth in performance according to UC Berkeley's LMSIS benchmark. → Technical Innovation in Inference Transformed computational problems into memory-oriented tasks. Built multi-layer caching system and specialized inference engine. Achieved inference costs of 10 cents per million tokens – 1/30th of industry standard. → Engineering Focus Areas Prioritized GPU resource allocation. Optimized both training speed and inference efficiency. Developed custom inference architecture for maximum hardware utilization.
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Top Agent Applications: Cursor, Perplexity, Replit
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Top agent applications? #1: Cursor (
@cursor_ai ) #2: Perplexity (
@perplexity_ai ) #3: Replit (
@Replit ) What other ones would you say? Full survey here: https://
langchain.com/stateofaiagents -
Chinese AI Startup Trains GPT-4 Rival with Minimal Resources
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Chinese AI Startup http://
01.ai Trains GPT-4 Rival with Minimal Resources http://
01.ai, a Chinese AI startup, has trained its advanced model, Yi-Lightning, with just 2,000 GPUs and $3 million, compared to the $80–100 million OpenAI reportedly spent on -

Open Source AI: Benefits and Challenges at Slush 2026
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Our Chief Technologist & Co-Founder, @KunleOlukotun will be onstage at @SlushHQ on Nov. 20th. He’ll be speaking about the benefits & challenges of #opensource #AI alongside: @Thom_Wolf – @huggingface @graceisford – @Lux_Capital @ravmattu – @nytimes
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Neo launches first autonomous AI Engineer agent
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First look at @saurabhvij137 and @withneo, launched this morning.
— Robert Scoble (@Scobleizer) 15 novembre 2024
"First autonomous AI Engineer."
Unaligned #35. pic.twitter.com/V6zvDNsAqzFirst look at @saurabhvij137 and @withneo
, launched this morning. "First autonomous AI Engineer." Unaligned #35.