The leading Food CPGs are leveraging ML & AI Agents in supply chain for responsive forecasting; dynamic safety stock setting at SKU x location resolution; service level optimisers; autonomous replen, reorder & allocation. Now most are looking at Agentic scenario planning too.

Interesting debate last night… If you have: • the right vision / mission • the right people (driven, responsible, curious, hungry) bought into that mission • and the right incentive structures in place to motivate your people day to day to execute Do you need “managers”?

Nature is bad ass.

Anyone who genuinely thinks AI will cause mass job losses by 2027 has never worked in a mid-market or enterprise business. Data distributed across silo’d systems, change management challenges, process re-engineering, trust issues, high bar for governance, secure orchestration

Next level $hithousery from Hampton. Absolutely love it.

This guy nails the 3 ingredients for reliable enterprise agents. Clear, pragmatic, shippable. youtube.com/watch?v=kTnfJs…

This is interesting, and something most AI folk have intuitively felt when deploying AI agents across complex multi-step workflows or chained processes within enterprise environments.

Really enjoying SCOUTED’s data-driven football analysis ⚽️❤️ Subscribed via Billy Carpenter’s recent (excellent) article analysing Arsenal’s tactical evolutions. Data for the win. Keep up the great work guys.

Physical AI. Several devs I know are now moving to reskill at the intersection of software and hardware / robotics interactions, in fields like Agri and Biotech. Who are some leading folk I should follow in the Physical AI space? I like Brett Adcock Figure ..who else?

It seems like there is not enough of a policy response to the fact that, with 57M miles of data, Waymo’s autonomous vehicles experience 85% less serious injuries & 79% less injuries overall than cars with human drivers. 2.4 million are injured & 40k killed in US accidents a year

RAG is not dead! However, we are in an interesting phase of exploring unique ways to index and retrieve information. This vectorless RAG framework uses a tree structure index in place of vectors. Reasoning models will enable methods that mimic human-like search. Early days!

A new embedding model cuts vector DB costs by ~200x. It also outperforms OpenAI and Cohere models. Let's understand how you can use it in LLM apps (with code):

In the last few years we've seen: - The plot to kidnap Gretchen Whitmer - The Storming of the Capitol and pipe bombs left at the RNC and DNC - The break-in to kidnap Nancy Pelosi and the brutal on Paul Pelosi - Multiple assassination attempts against Trump - The assassination

its so insane they turned Charlie Kirk into an actual martyr for free speech. not to glaze Kirk as I really didn't agree with anything he stood for but who else in history was killed sitting in front of a crowd, allowing anyone at all to step up and debate him for fun

CNN’s Jake Tapper: “It’s so easy in this world. Everybody just demonizes everybody else. And — and look, Charlie was in — in the battle. He was in the political rough and tumble too but these are human beings. And, you know, they don’t eat, breathe and sleep politics. I mean,

In this age of de-sensitisation to violence, absence of critical thought, extreme polarised views with minimal good faith debate…. …. Charlie Kirk’s murder is an absolute travesty for Western civilisation, regardless of where you land on the political spectrum.

This seems like a pretty big deal. arstechnica.com/space/2025/10/…

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