407 summarised stories about AI Coding Agents, each linking back to the original source. Browse all topics →
TechCrunch 3 months ago 41
Cognition acquired Poke, an AI messaging assistant known for conversational personality, in a deal valuing the startup in the low nine figures. Poke users exchanged over 100 million messages in the past three months before the acquisition. Cognition plans to integrate Poke's interaction style into its coding assistant Devin and use its own models to make Poke faster and more reliable.
Substack 3 months ago 15 ● 2 sources
The author argues that speaking to AI code assistants rather than typing prompts—what Andrej Karpathy calls "ramble sessions"—preserves more of the reasoning and nuance behind decisions, allowing the agent to understand the user's priorities and constraints. The approach yields fewer follow-up prompts and better outputs because the AI captures uncertainties, emotions, and trade-offs that typed prompts flatten into lists. Users can amplify this by rambling with coworkers, recording conversations, and transcribing them with Whisper before feeding them to the code assistant as a single context blob.
GitHub 3 months ago 45 ● 2 sources
A software engineer argues that fully autonomous AI code generation (lights-off software factories) fails because AI models cannot maintain codebase quality over time, despite excelling at benchmarks and rapid development; the author's company attempted this in July 2025 and experienced multiple severe outages within months that required manual intervention. The Faros AI report found that since January 2025, pull-request review quality dropped significantly, with incidents and bugs per developer increasing substantially after teams adopted AI coding tools. Companies must balance AI velocity with human oversight of code maintainability, as no amount of prompt engineering or automated testing can solve the fundamental limitation that models degrade codebase architecture without continuous human steering.
X 3 months ago 31 ● 3 sources
Victor Taelin proposed a code review process that focuses on auditing AI agent architectural decisions instead of line-by-line review of generated code. The method concentrates on examining the meaningful choices AI makes during code generation, reducing review overhead. This approach allows developers to catch significant architectural issues while accepting that AI-generated implementation details require less scrutiny than human-written alternatives.
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26 October 2026
Europe’s AI story today is capital flow with a purpose: where investors put money when they don’t yet see economy-wide payback. Q3 2026 saw European tech raise €27.8 billion across 732 deals, up 32.4% from Q3 2025, with AI accounting for €6.7 billion—about 24% of the total. Callosum’s €100M seed round for AI infrastructure software sits near the center of that tilt, underscoring a market still prioritizing the plumbing. In parallel, Apollo’s chief economist Torsten Slok and Apollo’s broader analysis argue the financial benefit of AI remains concentrated in the “Magnificent 7,” where inflation-adjusted revenue per employee is roughly $780,000, versus about $527,000 for the other 493 S&P 500 firms. The message for executives is uncomfortable but practical: until AI shows up in measurable productivity, budgets become cost-center negotiations.
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