u/ExplorerOk6989 — reddlx

UK Growth: Boring Wins

I have spent this week in the US with ministers, business leaders, academics and creatives. The theme is growth and collaboration, but one thing really resonated and gave me some hope, a talk with Blair McDougall, the UK's Parliamentary Under-Secretary of State for Small Business and Economic Transformation within the Department for Business and Trade. He was passionate and articulate, but also clear that the government is being unapologetically long-term in its thinking and strategy. I hope this is true, I penned these thoughts along the same lines, but also to highlight that much of what we need isnt headline grabbing, and that is ok....

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u/ExplorerOk6989 — 17 hours ago

▲ 2 r/Innovation

What is Data?

Most CEOs are making the most expensive strategic decision of the decade. Most don't know they're making it.

In 2020, AI training data was free. By 2024 News Corp got $250M for it. Reddit makes $130M a year. The FT, Axel Springer, the AP, all paid.

The lesson generalises. In the agentic era, your data is the most important strategic asset your company has. Almost no board is treating it that way.

Played offensively, it's a moat.

Played defensively, it's survival.

Played not at all, it's the bill that arrives later.

This week in Ideas for a Better World: how to make the choice deliberately.

What's your data strategy?

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u/ExplorerOk6989 — 8 days ago

▲ 2 r/Innovation

I've been finding it hard to keep a clear picture of all the new AI tools, and I know I'm not the only one. Terms like RAG, Agents, and LangChain get thrown around, but the mental model never quite stuck for me. Until I started thinking about it like this:

An LLM (like GPT) is a brilliant consultant. It knows a huge amount, but it has total amnesia. It forgets everything the second your conversation ends, and it can't look anything up. Just text in, text out.

All the tools are just systems to make this consultant useful.

RAG (Retrieval Augmented Generation): You hire a super-fast researcher to sit next to the consultant. When a question comes in, the researcher grabs the *perfect* internal documents and puts them on the desk. The consultant then answers, using that fresh information. It's giving the model the right context.

Agents: Instead of giving the consultant a fixed, step-by-step task, you just give it a goal (e.g., "research our competitors") and it decides its own steps. It can reason, act, observe the result, and loop until the job is done. This is where tools like LangGraph come in, to handle workflows that aren't just a straight line.

LangSmith (Observability): When your AI system gives a weird answer, how do you know why? Did the consultant hallucinate or did the researcher bring the wrong documents? Observability tools let you see every step of the process, so you can actually debug what's happening.

Thinking of it this way has really helped cut through the noise for me. Hope it helps someone else too.

I wrote this up in a bit more detail in a recent blog post: https://infinite-loop.co/blog/its-ok-to-feel-lost-in-ai-right-now

u/ExplorerOk6989 — 29 days ago