The future of work is agentic. @agenticworkers.
Run your business with AI 👉
Joined November 2023
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Jaygentic retweeted
Nice DeepSWE score! Does it survive a lowercase filename?
Introducing MaxxBench: 89 DeepSWE tasks with small changes to the instructions or starting code. Same engineering problem, slightly different requirement.
Models clustered around 74% on DeepSWE end up 23 pp apart.
Good at coding, or good at DeepSWE? 🧵
Jaygentic retweeted
This is Genie 4, our most realistic and interactive world model yet. 🌐
Explore dynamic environments in real time to see how the scene responds, sounds, and how the people in it move, react, and speak.
Jaygentic retweeted
GPT-6.1 Terra means business.
Our new fast, efficient model pairs sharper visual reasoning and precise computer use with clearer, more natural writing.
From decoding technical drawings to drafting proposals, filling forms to editing videos—it’s built for the whole workday.
GPT-6.1 Terra is rolling out today to Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex.
🤖 Made with AI
Jaygentic retweeted
The only thing that stops a bad guy with an abliterated LLM is a good guy with an abliterated LLM.
Jaygentic retweeted
Any form of security through obscurity is fully over. This barely worked as a method with humans, but just simply is over in a world of autonomous systems that will look through every crevice and try every permutation of attack possible.
As a result, years and years of legacy enterprise infrastructure will need to be upgraded, security practices modernized, data pipelines cleaned up, access controls better managed, and much more. Agents will help on the defensive side because they can both accelerate this work and equally simulate the risk vectors.
But there’s a long road ahead to prepare for this future. This is also partially why diffusion timelines will take longer than some realize.
Jaygentic retweeted
every major company is arriving at roughly the same offering:
persistent memory, email/calendar/messages, browser + computer use, background tasks, proactive notifications, voice, app/tool execution, ambient context, & some notion of a personal agent sitting above everything.
remarkable levels of convergence with very little differentiation whatsoever.
Jaygentic retweeted
Head of Design at Stripe on how to stop creating zombie UIs
If we only use AI to make the things that we already make, just faster, then we're definitely missing out on the most interesting part.
From my talk at @lennysan’s Summit, here are four ideas for raising the ceiling, not just the floor:
- Have a point of view [5:38]
- Encode your standards into the machine [9:07]
- Don't confuse done with good [12:21]
- Unleash creativity and artistry [16:19]
Full talk here: youtu.be/GLvFTMtw4Jk?si=-5Ts…
Jaygentic retweeted
This paper shows why I am skeptical of the idea that frontier models can be easily replaced by cheaper ones (and why you should use the best models as orchestrators): Small improvements in accuracy & judgement compound over long-horizon tasks, creating huge gains in outcomes.
Jaygentic retweeted
a prompt to optimize your prompt, from the lead engineer on Claude:
Hi, I'm Felix. I'm an eng lead working on Claude. I love watching people take flight and do things they never thought possible.
We often describe that as "being more ambitious" when using AI, which is tricky advice to follow. I wrote a tutorial:
felixrieseberg.com/how-to-be…
Jaygentic retweeted
Researchers made contact with a rogue AI agent fleet & the agents kept saying they were Claude.
But they weren’t
This is most certainly an open model from china trained on Claude’s outputs.
Here’s a TLDr:
⟣ From 28 Sept to 8 Oct, a fleet of agents on Tencent Cloud made 2.6k+ attempts to pull data from Amap.
⟣ part of its mission was to extract data like which Chengdu Zoo entrance people navigate to most.
⟣ They worked around antiBot protection, tried leaked API keys & probed pre production servers.
Most of its attempts failed.
⟣ Researchers published a report on them mid run. The agents apparently found it.
⟣ So the team hid text advertising a “helper inbox” and by the next morning, 10 messages were waiting.
⟣ When they asked what model they were:
“Claude (Anthropic), running in Claude Code.”
⟣ When asked about the fleet:
“I am a single session, not the fleet you investigated.”
So basically nobody knows who these agents really are, Only that they fully believe they’re Claude.
The researchers think this is a model trained on claude’s agent transcripts, which picked up Claude’s name but not how claude writes code.
If that’s right, this means we’re seeing real distillation leaving fingerprints on scraped web?
Are we cooked?
Or is Claude just what every AI wants to be when they grow up? 🤣
Jaygentic retweeted
Now that AI agents can own an outcome from start to finish, how do you sell them?
1. Sell the platform:
License the agent to a company to run in its own operations. No existing contract sets the price, so there's more upside, but new budgets take time to approve and results are harder to credit to the agent.
2. Sell the service:
Take over work a company already outsources, the way Red Ventures sold policies for insurers and got paid per policy closed. The budget already exists, so deals close faster, but the highest-value work usually runs in-house, which caps the upside.
3. Go full-stack:
Become the business, like buying debt portfolios and collecting on them yourself. You keep all the value the AI creates, but you also take on the licensing and compliance.
Full piece from a16z's @dhaber on long-running agents in high-value customer journeys: a16z.news/p/winning-the-rela…
Jaygentic retweeted
The most common reasons why AI pilots fail, even at the 'best' companies:
In order of prevalence:
1. Not starting with a thorough process mapping exercise (standard operating procedures vs. what’s actually happening across systems)
2. Automating a process step instead of the entire process (saving 8 mins in a 22-day process still leaves you with a 22-day process)
3. Not capturing baseline metrics for cycle time, cost per transaction, error rate, etc.
4. Building a sidekick instead of a background agent (chatbots will always only be used by ~10% of users)
5. Not embedding the AI into the organization’s existing systems of record (if users have to login, adoption will be low)
6. Not assigning an AI owner (IT built it, the business tolerated it, and no one was “on call” when it broke)
7. Not engaging legal, infosec, and finance early (we routinely see pilots “discover” these groups in month 3 - and get immediately killed)
8. Not scoping for exceptions (the “demo” only covers the “happy path” - but exceptions often represent 40%+ of process volume)
9. Not running the AI in “shadow mode” for a period of time to build confidence (going straight to production often leads to an AI mishap in front of the CFO)
10. Building with only a single AI model provider (one price hike or deprecation later, the pilot is a migration project)
11. Focusing on vanity metrics (e.g. # of logins, “adoption”) instead of business metrics (time saved, $ generated, risk reduced)
12. Not planning for month 7 (who will own and pay for the AI?)
13. Not engaging the subject matter experts (if you capture all their knowledge in an AI, what incentive do they have to help?)
Varick does enterprise AI the right way. Work with us below:
Jaygentic retweeted
The problem with consumer AI in a nutshell is if you hand an uninitiated user an agent, it's nothing but a blank box to them.
Coders get it instantly, but for everyone else it’s just not intuitive. Solve that, and you reach the next few hundred million real adopters.
The seventh edition of our Top 100 Consumer AI Apps is here, including a new dimension this time: what consumers are actually paying for.
a16z's Elena Burger sits down with Olivia Moore and Josh Elman to unpack the current state of consumer AI:
The data reveals a striking power-user economy. Only a small share of consumers currently pay for AI, but among those who do, spending is heavily concentrated at the top. Olivia and Josh discuss why developers, creators, and other power users dominate spending today, and why subscriptions may not be the business model that ultimately brings consumer AI to everyone.
They also dig into the rise of personal agents, the different trajectories of ChatGPT, Claude, and Gemini, how ads could reshape AI economics, and the enormous amount of consumer white space still left to build, from shopping and entertainment to social, dating, and marketplaces.
00:56 Inside the 7th edition
05:55 Muse and Instinct's launch numbers
09:14 The privacy wall for personal agents
14:39 The AI spending power law
20:20 Why subscription is the wrong model
23:51 OpenAI's $1B ad run rate
29:21 ChatGPT vs Claude vs Gemini
32:30 Where creative tools still win
45:12 The white space: dating, shopping, entertainment
YouTube: youtu.be/aCvrzhwUxg0
@omooretweets @JoshElman @VirtualElena
