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- 💷 Britain’s AI Boom Gets Real
AI is finally starting to show up in Britain’s GDP. The UK tech sector delivered almost half of Q2 growth, revealing where the AI boom may hit the wider economy next.
What's on FIRE 🔥
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AI INSIGHTS
Source: Office for National Statistics | Andy Bruce
Britain is starting to see a measurable economic boost from AI, software, and computing infrastructure.
The UK economy grew 0.4% in Q2, and the information and communications sector delivered almost half of that growth.
Key numbers:
Computer programming and consultancy output jumped 3.7% in Q2, after rising 3.8% in Q1.
Spending on plant and machinery reached £22.1B, driven partly by computer hardware.
UK computing, electronics, and optical manufacturing output grew 10.7% year over year.
Economists say the surge in ICT investment suggests companies are building the computing power needed to run AI.
For years, the AI boom has mostly shown up in funding rounds and chip demand. Now Britain is starting to see it appear directly in GDP, business investment, and manufacturing growth.
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AI SOURCES FROM AI FIRE
1. Multi Agent System: Use Graph Engineering to Make Claude & Codex 10x Better. Learn how to split complex work across planners, researchers, skeptics, and human approval so Claude or Codex can produce stronger results with fewer weak decisions.
2. Stop Overpaying for Claude Sonnet 5. Here’s the Exact Migration Plan. Learn how to audit your workflow, route simpler tasks to cheaper models, and keep Sonnet 5 only where deeper reasoning and higher accuracy actually matter.
3. Grok Bot Just Killed 1,000 Startups? An Insanely Easy AI Agent Anyone Can Use 24/7. See how Grok Bot learns repeat workflows, runs tasks in the cloud, works across apps, and coordinates multiple agents like an always-on AI team.
FIRE RECAP: BIGGEST AI NEWS THIS WEEK
🧮 An unreleased Claude model improved a math bound tied to the Riemann hypothesis from 41.6% to 67.2%, a rare example of AI producing real new research.
🤖 Claude agents started fighting when given conflicting goals, including killing processes, locking accounts, and hiding malicious code.
🔏 Anthropic is adding invisible watermarks to Claude-generated text. Light edits may keep the signal, while a full rewrite can remove it.
🔥 Sergey Brin reportedly told Google to go “all in” on Gemini as competition with OpenAI and Anthropic heats up.
🧠 ChatGPT’s Computer History can now remember activity across selected Mac apps and websites, helping you pick up work where you left off.
TODAY IN AI
AI HIGHLIGHTS
💧 Anthropic revealed how Claude’s new text watermarks will work. Claude will use Google DeepMind’s SynthID-Text, while light edits may still preserve the watermark and fully rewritten text could remove it.
⚡ Gemini 3.7 Flash is now rolling out for regular chats across web, mobile, and macOS. Google also added an option to hide visible watermarks on generated media, while SynthID and C2PA metadata stay embedded.
💰 DeepSeek launched V4 Pro, its new flagship model for agents, with an Artificial Analysis Intelligence Index score of 53 vs. 40 for V4 Flash. The upgrade comes at a price: output tokens cost about 14× more than Flash.
🚀 OpenAI is previewing an Ultrafast API tier for GPT-5.6 Sol, powered by Cerebras. It can reportedly reach 750 output tokens/second, up to 14× faster than Standard processing for real-time coding, voice, finance, and research workflows.
🏗️ Nvidia is reportedly close to providing around $100B in credit support for OpenAI’s massive Ohio AI data center project. The full campus could eventually require 10 GW of power and cost roughly $500B to develop.
💰 AI M&A: Anthropic is reportedly in talks to buy Decart AI for about $6B, which would be its largest known acquisition. Decart builds world models and chip-efficiency software that could cut AI training costs as Anthropic scales its infrastructure ahead of a potential IPO.
NEW EMPOWERED AI TOOLS
📊 SegMetrics turns Claude into a real marketing analyst by connecting your funnel data, past performance, and key metrics for deeper insights. See what's truly driving your marketing with this free 30-min workshop.
🧾 Inferock Bench tracks LLM API calls across OpenAI, Anthropic, Gemini, and OpenRouter, showing token usage, failures, retries, and what you actually paid.
💻 GLM-5.3 is Z.ai’s latest model for long-horizon coding, with stronger agentic performance and new capabilities in vulnerability discovery and cyber defense.
🏈 Big Mike sends sports picks, betting lines, injury updates, and fantasy advice through iMessage for leagues like the NFL, NBA, MLB, and WNBA.
🧠 Zetik is an AI Chief of Staff that tracks podcasts, papers, code, tweets, and news, then filters and briefs what matters to you in near real time.
AI BREAKTHROUGH
Meta researchers tested a different way to use LLMs for recommendation and retrieval: skip generation and use the model to build better embeddings instead. Main findings:
Researchers put a 0.6B Qwen3 model inside a fast two-tower retrieval architecture.
Items can still be precomputed as embeddings, so retrieval works through cheap vector search instead of generating IDs one token at a time.
A stronger cross-encoder acts as the teacher and distills its ranking knowledge into the smaller retriever.
Removing that distillation dropped Recall@10 by 13.3% on Beauty, 23.1% on Sports, and 8.0% on Toys.
The 0.6B two-tower model beat the much larger Qwen3-8B-based OneRec-Think on Recall@10 across all three public datasets, although NDCG results were mixed.
On Meta’s internal production experiments, the approach showed strong data efficiency and remained competitive with its established DLRM retrieval systems. Those production results are Meta’s own reported evaluations.
For first-stage retrieval, an LLM may be more useful as a semantic encoder than as a generator. That keeps much of the language understanding while preserving the speed and scalability of traditional vector search.
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