Patrick “pH” Hampton

Quotient Intelligent - Inventor of Recursive General Intelligence (patent pending)

[Patrick “pH” Hampton @ChannelpH]

The 1st equation of AGi is
Q(t+1)=Q(t)+🔺Q(t) #AGI #pH #Ai

[Patrick “pH” Hampton @ChannelpH]

Agentic systems do not become intelligent by multiplying agents. They become intelligent when a recursive control layer can identify, stabilize, and advance useful quotient updates while detecting blind spots in the proposal field. #AGI #pH #Ai

[Patrick “pH” Hampton @ChannelpH]

@rohanpaul_ai @rohanpaul_ai I mean… proto-AGi is really cool to play with. I’ve been doing it for a while.

[Patrick “pH” Hampton @ChannelpH]

Y’all are killing me with some of these paper titles. Can we get some originality. We are Quotient Intelligent are one of the worlds first AGi companies that hold proofs-of-principles and have actively working AGi Demos. There is still a tech gap for true AGi but proto is here.

[Rohan Paul @rohanpaul_ai]

Is Grep All You Need?

The surprising result is not that grep is powerful, but that agent design makes it powerful.

The paper says not that grep beats vectors, but that agents fail or win through their harness.

That sounds like a small distinction until you look at what was actually tested.

The authors compare grep-style search and vector retrieval across LongMemEval tasks, where agents must recover facts from long conversation histories full of distractors. Inline grep beats inline vector across every harness-model pair in their main experiment, sometimes by wide margins.

The tempting headline is that vector databases are overbuilt for coding agents.

The better reading is sharper: when the answer is anchored in literal evidence, names, dates, file paths, function names, error strings, user preferences, grep gives the model a clean mechanical advantage.

Embeddings are built to tolerate paraphrase, but tolerance has a cost. They can pull in semantically nearby clutter, especially when a short agent query is vague.

Grep has the opposite failure mode. It is dumb, cheap, and narrow, but when the agent knows the right string to hunt for, dumb becomes a feature.

The deeper finding is that retrieval is not a component you can benchmark in isolation. The same search method behaves differently depending on whether results are injected inline, written to files, routed through a CLI, or wrapped in a custom agent loop.

So the question is not “Do we still need vector databases?”

The question is whether your agent is solving a semantic discovery problem or an evidence-location problem.

For coding agents, a surprising amount of work is evidence-location: find the symbol, trace the call, inspect the diff, read the failing test, recover the exact line.

Vectors still matter at scale and for fuzzy conceptual search, but this paper weakens the lazy default that every serious agent stack begins with embeddings.

Sometimes the upgrade is not a smarter index.

Sometimes it is giving the model primitive tools, clean files, disciplined context, and a harness that lets exact search do exact work.

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Paper Link – arxiv. org/abs/2605.15184

Paper Title: "Is Grep All You Need? How Agent Harnesses Reshape Agentic Search"

[Patrick “pH” Hampton @ChannelpH]

I’m pretty sure I’ve been saying this for years. 🤦🏼‍♂️

[François Chollet @fchollet]

Decision making was the bottleneck all along. Productivity is the rate at which you make open-ended decisions, the rate at which you reduce future paths.

[Patrick “pH” Hampton @ChannelpH]

@fchollet Im pretty sure I’ve been saying this for years. And, might if sent you an email. 😅 “Agentic topology only becomes AGI when governed by recursive quotient stability laws.”

[Patrick “pH” Hampton @ChannelpH]

@haider1 Agentic topology only becomes AGI when governed by recursive quotient stability laws.

[Patrick “pH” Hampton @ChannelpH]

I disagree. We are seeing positive results with vibe coding the @arcprize test as we speak. #agi “Agentic topology only becomes AGI when governed by recursive quotient stability laws”.

[Greg Kamradt @GregKamradt]

AI won’t do the work for you

[Patrick “pH” Hampton @ChannelpH]

@GregKamradt @GregKamradt my math is vibe coding the @arcprize 1 right now. With positive results. I’m sorry but I disagree. #AGi “Agentic topology only becomes AGI when governed by recursive quotient stability laws”.

[Patrick “pH” Hampton @ChannelpH]

@haider1 We found out you about need about 30% qualifying data and you can nudge a model into a professional from there pretty quick.

[Patrick “pH” Hampton @ChannelpH]

“Q2E governs non-canonical mechanism-spaces by assigning quotient-state values to competing internal pathways.”

[ARC Prize @arcprize]

GPT-5.5 & Opus 4.7 on ARC-AGI-3

- GPT-5.5: 0.43%
- Opus 4.7: 0.18%

We found 3 failure modes:
- True local effect, false world model
- Wrong level of abstraction from training data
- Solved the level, didn’t reinforce the reward

See our full analysis 🧵

[Priyanka Vergadia @pvergadia]

🤯BREAKING: Researchers just mathematically proved that AI layoffs will collapse the economy: and every CEO already knows it.

The AI Layoff Trap. A game theory paper from UPenn + Boston University is glaringly important!

100K+ tech layoffs in 2025. 80% of US workers exposed. And no market force can stop it.

→ Every company fires workers to cut costs
→ Every fired worker stops buying products
→ Revenue collapses across every sector
→ The companies that fired everyone go bankrupt

It's a Prisoner's Dilemma with math behind it. Automate and you survive short-term. Don't automate and your competitor kills you. But everyone automating destroys the demand that makes all companies viable.

UBI (universal basic income) won't fix it.
Profit taxes won't fix it.
The researchers found only one solution: a Pigouvian automation tax "robot tax"

The AI trap on the economy is here!

[Patrick “pH” Hampton @ChannelpH]

Demo of MVP "After-Life: The Drifter" showing off NPC mechanics. Watch Quotient Intelligent in action! https://t.co/POv3it8UoL via @YouTube

[Patrick “pH” Hampton @ChannelpH]

@abxxai this is also the long context horizon problem. Which has been figured out and is addressable with a governance layer. #QiAGi

[Patrick “pH” Hampton @ChannelpH]

@elonmusk @elonmusk and my work fixes it. Ironic. Here is a link to one of many demos we have https://t.co/UWkvta53VU or visit https://t.co/Swmxr0SXAK to check out our first product. #QiAGi

[Patrick “pH” Hampton @ChannelpH]

@rohanpaul_ai and you might find this interesting.

[Patrick “pH” Hampton @ChannelpH]

If this is true. @ylecun we have a lot of talking to do.
https://t.co/LlmoYZ45Lk #Qi #AGi #JePA

[Patrick “pH” Hampton @ChannelpH]

@rohanpaul_ai you might find this interesting.

[Patrick “pH” Hampton @ChannelpH]

Check out our new website. https://t.co/vfQHoN44uA
#Qi #AGi #pH #Ai

[Patrick “pH” Hampton @ChannelpH]

@fchollet You might find this interesting

[Patrick “pH” Hampton @ChannelpH]

@fchollet you might find this interesting, too. If this is real. I have a lot of explaining to do. Including to the ArcPrize.

[Patrick “pH” Hampton @ChannelpH]

Qi AGi has launched it first product. An Ai Screenwriting Platform called CanIScreenwrite! Click the link and sign up to get early access today! Season Zero starts soon.

https://t.co/Swmxr0SXAK