experience

The author shares their experience watching a fully AI-generated sitcom, noting a split attention between spotting AI artifacts and enjoying the comedy, and inquires about similar experiences from others.

The article shares lessons learned from operating petabyte-scale ClickHouse clusters for five years, discussing best practices and promoting Tinybird's managed data services.

The article argues that while AI tools like Claude and GLM-5.3 offer a 10x speedup in code generation, the overall development process is bottlenecked by specifications, reviews, and QA, making the actual delivery improvement less significant than claimed.

A VP/PM with coding background shares hands-on experience using LLMs like Claude Opus and Fable, highlighting limitations in memory, hallucination, and originality while emphasizing the irreplaceable value of human intuition and domain expertise.

A firsthand account of building a team of AI agents at a company using the Hermes framework, detailing practical outcomes and lessons learned.

Sam Altman predicts that success in startups will increasingly depend on proficiency with AI tools rather than years of traditional experience.

A user shares their early experience with Opus 5, likely an AI model release.

A tweet shares an anecdote about a fast-growth startup where young product managers lack the attention span to read PRDs, reflecting on changing product management practices.

An article sharing hard-won lessons from building a web agent that can interact with real web applications, offering insights into challenges and best practices.

Birgitta Böckeler shares her experience running local LLMs for coding tasks, outlining factors like RAM, response speed, tool calling, and quality of outcomes that influence their viability.

A Twitter post shares an anecdote about interviewing an AI engineer who lacked knowledge of latest frameworks but excelled at debugging a real-world performance issue, emphasizing the value of practical problem-solving over tool familiarity.

An article discussing the real pros and cons of using no-code AI agent builders, based on the author's experience building over a dozen such agents.

A personal reflection on using AI coding tools (Codex) to build an app, with ten takeaways from the experience.

The author reviews three months of experience using multi-agent collaboration, summarizing five main pain points (such as conflicts between agents, ignoring boundary conditions, self-censorship failure, difficulty in merging decisions, and exposing harder problems after compressed execution) and two insights (the high value of read-only review agents, and that agent conflicts expose ambiguous requirements), emphasizing the core decision-making role of humans in AI collaboration.

Mark Zuckerberg argues that experience is overrated in hiring, emphasizing raw talent and side projects. He uses Facebook's hiring of fresh graduates and a CFO with no IPO experience as examples.

A programmer shares his experience using the Onyx BOOX Mira Pro Color e-ink monitor as his primary display for coding, including custom themes and open-source tools to improve usability.