Kamdoum Ngamgoum Franck Junior(Sabitou)

A Trillion Dollar Context Economy. New software category to reshape the world’s software industry. Context is King You’re welcome.

@rronak_ People finally nailed the true definition of context, called it harness. Please, this is just “context 2.0”. And context 3.0, 4.0, 5.0 and even 6.0 existed too.

I have long felt that agent harnesses - even claude code - are too restrictive, because they are still designed by humans. New paper for Tinsghua and Shenzhen says, what if AI itself runs the harness, rather than defining it in code? Given a natural language SOP of how an agent should orchestrate subagents, memory, compaction, etc., we can just have an LLM execute that logic! (And AI could design that SOP dynamically and depending on the task too) It's a bit mind-warping to think about, but genius once it clicks. Makes you wonder how else we should be designing AI systems as we can start consuming more and more tokens

It’s wild to think about what types of infrastructure and services must change in a world where agents can process information a hundred or a thousand times faster than humans. Even the tools that were built for machine speed before, generally were still in service of end-users making a request somewhere in the system. Agents running 24/7 and in parallel modify these requirements meaningfully. Here are just a few examples: * Sandboxes. Agents need sandboxes to operate in that have to be insanely low latency because they can boot up these environments for coding at any moment. * Search (both publicly and within an enterprise). Agents can parallelize searches hundreds or thousands of times so they need to be able to work with fast indexes of information. * Payments. Agents can now pay in micro transactions, and aren’t bothered by the friction of paying $0.01 for a resource that a human would be. * File systems. Agents need to be able to work with files at a scale that humans never had to worry about. You’ll have all new complexity around version control, permissions, and having agents reading/writing from data at insane speeds. And there are tons more. We’re going from a word where software was built for people to a world where it’s built for agents. Lots of changes downstream as a result.

Jeff Dean says we’re going to have to re-engineer our tools because they were designed for human speed. An AI agent can run 50x faster, but the tools it relies on don’t. So even if the model gets infinitely fast, you only get 2-3x improvement overall. Amdahl’s law still applies.

In Game of Thrones Saga, I’m the guy who wrote The Song of Ice and Fire. Don’t get me wrong, I can rewrite the full scenario based solely on my desires and still shape the future.

Happy Week Builders. Build Different.

The thing that most people miss initially with agents is that the scope of what we will produce will go up commensurate with what the tools can now automate, which basically means we’ll working the same or even more. Everyone thinks that we will use AI to do what we already do but cheaper and faster, which would lead to fewer people or getting more time back. In fact it will just mean we’re doing more things. Once we figured out that we can automate a particular task, you then expand the size of work to do many more of those or other tasks in a project. The result is that you’re actually combining many other previously hard to combine tasks into a single workflow, causing even more work. The software project scope now multiplies because you know you can build far more. The customer insights project now balloons because you know you can reasonably aggregate far more data. The marketing campaign has even more creative production because it’s cheaper and easier. This is going to happen in almost every field of work.

Bye. Ciao.

We need Transformers for AI agents, agentic systems, Long-horizon tasks, self-improving systems.

In Frontier tech, mission is the moat.

Le meneur de jeu🔟 c’est TPKING👑🧠

Vous voyez que tout ce que TPKING represente vous manque. Je suis l’unité de mesure👑🔟🌍

Context is King.

No dating until Kardashev 3😹.

Thanks you Universe for keep running things smoothly

Let your yes be a yes. Let your no be a no.

@ClementDelangue We need transformers for agents first

We need more open agent traces datasets. Who can help?

Chers Africains, finissez votre propre bataille. Comment savoir qu’on n’a pas fini ses propres batailles? Lisez 👇🏻

The big gap in most enterprises being able to automate work is being able to get right context to the agents. We experience a huge benefit in coding in tech because the problem is generally far simpler than other areas of knowledge work. The codebase contains a bunch of necessary context, access controls and permissions are generally not a major concern, the users are technical enough to supply the context, and the final output is generally quickly verifiable. Most knowledge work doesn’t look like this. The data is sitting in legacy silos that don’t easily connect to agents, the access controls are all out of whack (people have either too much or too little access), the information isn’t agent-ready, and more. This is the big context gap for any type of agentic workflows in most organizations right. The platforms that make solving this easy, and the companies that retool their workflows to enable this, will be the winners in a world of agents.

Context is now (and perhaps always been) the bottleneck on growth, because anything with full context can be automated. The race is on to make the collective brain of the organization legible to AI

Curiosity First. Conviction Next.