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  1. Page 1: Today’s IndexTLDR, TLDR AI, TLDR Dev, TLDR Design, TLDR Founders · 9 stories
  2. Page 2: TLDRTLDR · 10 stories
  3. Page 3: TLDR AITLDR AI · 12 stories
  4. Page 4: TLDR DevTLDR Dev · 7 stories
  5. Page 5: TLDR DesignTLDR Design · 12 stories
  6. Page 6: TLDR FoundersTLDR Founders · 11 stories
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  8. Page 8: Resources & ToolsTLDR, TLDR AI, TLDR Dev, TLDR Design, TLDR Founders, TLDR Marketing · 9 stories
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Daily Edition · Page 6

THE DAILY INDEX

Technology, AI, software, security, crypto and business

HEADLINES & TRENDS

TLDR Founders

TLDR Founders

SOFTWARE IS EATING THE WORLD (BUT ACTUALLY THIS TIME)

AI agents now execute the actual work as code loops rather than just mediating interfaces, converting human jobs into tool calls and verification steps. Agentic tasks burn orders of magnitude more tokens than chat because a single Claude Code bugfix can consume around 900k tokens, where almost all goes to context replay and tool output rather than visible code. METR data shows autonomous task horizons doubling every 131 days, jumping from 4 minutes on GPT-4 to roughly 12 hours on Claude Opus 4.6 and pushing OpenAI past 15B tokens per minute alongside Google's 50x YoY token growth.

links.tldrnewsletter.com · 11 min

TLDR Founders

THE FOUNDER'S GUIDE TO BLOGGING

Polished prose used to take effort. Now, any founder can produce a clean feature announcement in ten minutes, but most of them read like the same announcement. Swap your company name for a competitor's and read your last post again. If it still makes sense, you wrote a press release. Don't hand the model a feature spec and ask for a polished post. Hand it the customer ticket, the Slack argument, and the voicemail where you explain why this even matters.

speedrun.substack.com · 8 min

TLDR Founders

DIRECTION OVER SPEED

For most of software's history, you could fake productivity by shipping a lot. AI broke that cover. Prototypes are cheap, specs write themselves, and shipping looks the same whether you're building the right thing or the wrong one. Smaller teams of four or five, with the PM closer to the work, especially on AI features where "is this good" can't be answered by a metric. The model can build the thing for you. It still can't tell you why anyone should care.

leahtharin.com · 7 min

TLDR IndexPage 6