Alan Garcia 🇺🇦 @alangnative

Dear GrokBot team, is there a kb shortcut for switching between accounts/workspaces akin to Slack's `⌘<n>` shortcut. Tysm

An email from [email protected] landed in my inbox, passed SPF, DKIM, DMARC, and had the blue check Something felt off, so I asked @bot to do a security review. It found a hidden phishing link, explained why every safety check passed, wrote a report to xAI - including proposed fix!

Can't even find the WhatsApp search function

Filing Complaint that the latest WhatsApp update on MacOS does not support native macOS Split View in full screen!!

One of the most important things to understand about startups is that because they grow intensely fast, they tend to induce a lot of anxiety in their founders due to perceived lack of control, and this anxiety manifests itself in various ways. A classic one is in projects that amount to ways to achieve greater legibility - hiring people to build dashboards, 'processes' to 'get their systems in order'. Almost always this is not actually addressing the real problem, which is inevitably a people problem. A perennial one is problems with sales discipline. Most salespeople are by nature crafty, ambitious, lazy, and somewhat bad with tech. They want to be on calls yapping all day and will do the bare minimum of admin that's tolerated by the business. As a result, most businesses have CRMs with terrible data quality. This results in the hiring of RevOps people, data engineers, more recently 'GTM engineers', etc. But it's fundamentally not a data problem. The problem is that the sales reps lack discipline and accountability. The founders or execs will convince themselves that if they can just get the perfect system in place with the right dashboards and fields, if they just figure out how to dedupe the data just right and build the right context layer, they'll be fine. But it's bullshit. They're avoiding the real solution, which is that they need to fire their underperforming reps and tell the rest of them that if they don't have their hygiene on point by the end of the month, they'll be fired too. In the past this was a hard ask to make because CRMs were legitimately terrible and updating them took a long time. But this is no longer the case. If you give every rep a Grok Bot, keeping the CRM updated is trivially easy. This is what actually effective AI transformation in GTM looks like, btw. Anyone who is leading with the tech is selling you a bullshit snake oil solution that will be tech debt in a few months. The real opportunity is in raising standards, driving massive amounts of urgency, and then using the new tech to find resourceful solutions to these new demands. Otherwise you'll just spin your wheels and build some pointless science projects

Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.

Andrej Karpathy @karpathy

6 months ago

Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is

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Need best iOS TTS/dictation app: reads email, web, bkmks, X threads/posts hands-free on-the-go.

More system 1 options available more reason to stop calling a slow, expensive generative model for every small decision that repeats. That's where the savings come from, and it makes the system simpler too. Put fast decision models in front to handle routing and triage, and you can afford to run them far more often: on every ticket, log line, incident, agent step, and pipeline run. Save the big models for the work they're actually good at.

Aakash Gupta @aakashgupta

a week ago

Microsoft just shipped an AI that makes decisions faster than you can blink. A blink takes about 100 milliseconds. This one takes 85. And Satya didn't pick this day by accident. A few hours before this post went up, a startup called TypeSafe closed an $870 million Series A at a

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@threepointone 😅🤣💀

Last week some of South Korea's biggest banks were hit by a cyberattack. Thanks to a report from CrowdStrike tonight, we now know the entire hack may have been done by a single person. He used a combined stack of an open-source AI penetration tool named ARTEX, DeepSeek v4.1-Flash, GLM-5.3, Grok 4.6, and Claude Code.

Quick review on how teams can triage work and speak to the approach. Rate every issue two ways: severity and priority. Severity is how bad it is. How broken, how many people, is data at risk. SEV1 to SEV4. Priority is when we fix it. We decide that from severity plus deadlines, customers, and what else is open. Example: a typo on an NDA gate is low severity, but high priority if it creates legal risk. Example: a slow internal batch job at 2am can be moderate severity, but it can wait until morning. So an approach is: rate severity first, then set priority.

Pithy on LinkedIn is super high signal

introducing claude motion! people have noticed how well Opus 5.5 makes animated explainers, dataviz and product demos — so we've built some lightweight ui on top. let claude animate, then point at part to edit. then export! super fun

Claude @claudeai

2 weeks ago

Claude Dashboards and Claude Motion are in beta today. Ask Claude to turn your data into live dashboards and your ideas into animated explainers.

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If thats too gloomy shine some light on it

A new industrial order

@Michael_Fenech_ Thanks. Found some dormant goals to kick the tires on. Also created a skill to invoke the audit at will

Putting Muse through the paces with a boring first task: Connect to Instagram and provide pre workout briefs based on saved posts. Not without its friction! Can't get Instagram to identify that my insta is already linked via Meta Accounts Center

The speed of our current landscape is nuts. Jev was groundbreaking for like 2 weeks. Cloudflare’s Clef outperforms it, adds vision support and 2x context window. Oh yeah, and it’s open source.

Mia @MiaAI_lab

3 weeks ago

Cloudflare's Clef beat Jev in just two weeks How: "frozen" Qwen does one prefill pass, a tiny schema head scores every answer option in parallel, with no text generated at all. That's why it's 4x faster than Jev at 2x the accuracy on some benchmarks. And it's open source!

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Accelerate with pstack and mattpocock skills do it

lauren @poteto

2 weeks ago

i had a lot of fun chatting with @mattpocockuk today about how i was able to land 2,500 PRs last month! Matt is a wonderful interviewer so i think the interview turned out really interesting both of our skill plugins work great together, so i recommend giving both a try and

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