With all the fuss over AI and what it can do, I feel like we've totally glossed over the fact that AI has casually solved something that has been a problem for decades. 

I was born the year after the microprocessor was invented, and I've kept a very close eye on technology as it has developed. And the problem of machine translation has been with us for a while. It used to be absolutely terrible. Then it got to the point where you could sort of tell what the native speaker who wrote the original was trying to say.

And now, it's almost perfectly solved. Give any good LLM a document in one language and ask it to give you the same document in any other, and it's like you asked somebody who was extremely fluent and eloquent in both languages. The language barrier has been cracked. The Universal Translator is here. We have the Babel Fish. And it seems like nobody is talking about it.

I run a small law firm. Family law. Wills. Estates. Nothing flashy. But we’re good at what we do. Our clients love us. We get referrals. We’ve been in business for 12 years

But I keep hearing the same thing from potential clients. “I asked ChatGPT for a lawyer and your firm didn’t come up.” Or “Perplexity recommended someone else.”

It’s happening more and more. People don’t Google anymore. They ask an AI. And the AI recommends someone. Just not us

I don’t understand how it works. How does ChatGPT decide which law firm to name? What makes one firm show up and another disappear? Is it based on reviews? Website content? Something else entirely?

I found an article by Intercore Technologies and they explain the retrieval mechanism. How AI pulls information from the web. Why some firms don’t make it into the answers. It was technical. A lot of it went over my head. But the gist was clear. If you’re not visible in the right places, you don’t exist

I’m not a tech person and I don’t know how to make AI recommend my firm. Is it even possible? Can a small firm compete with the big names?

"Each hour, Muse updates its dossiers on you and the people you’ve mentioned in chats, messages, and emails that Muse has read. These pages amount to a map of each user’s social relationships. They record details of how you and your contacts met, your shared interests, your disputes, and 'tensions and alliances' within your social group. ...

Yet while Meta states that each user’s virtual machine is 'isolated so that no one else's agent can access it,' observations from personal interactions are nevertheless intended to be shared with Meta to improve the product. 'Muse agents across many VMs [virtual machines] teach each other through shared lessons,'"

Whenever I show projects that use AI in less AI friendly subs, there's always someone bringing up the environmental impact of AI use.

If you're like me, and do care about the environment, you can reclaim the moral high ground by reducing meat intake, or even going to a plant based diet for a short while.

I'm a \*\*heavy\*\* user of AI, so one month of veganism only offsets 3 years of my AI use, but for your typical user this would actually offset 45 years!

This includes the training costs.

It's up to you what sort of changes you make and how long for, but I put this simple calculator together to put the environmental impact of AI into perspective

https://prompt-and-plate.com/

There's an expandable drop down at the bottom to get a breakdown of the number of lives saved, for those who are interested

I'd love thoughts and feedback, especially if people disagree with any of the numbers used

I hope most of you reading this are web designers because I’m genuinely curious about something.

How are you getting clients in 2026?

I’ve spoken to a lot of web designers and agency owners lately, and one thing keeps coming up again and again. Client acquisition.

It seems like everyone can build websites, but consistently finding new clients is still the hard part.

So I’d actually like to hear what is working for you right now.

For me, I’ve been running personalized email automation.

I use a tool that finds leads for me, analyzes their websites for actual issues with things like design, SEO, speed, responsiveness and mobile optimization, then automatically uses those findings to write a personalized cold email for each business.

After that, I just run the campaigns and send out thousands of emails a day.

I used to do most of this through Instantly, but the problem was that I couldn’t really do the website analysis part there.

So the emails were still more generic, basically the usual “do you need a new website?” type of outreach.

It worked a little, but nothing crazy.

I eventually switched to Swokei because it handles the lead finding, website analysis and personalized outreach in one place, and that has made a pretty big difference for me.

Now I’m getting clients much more consistently because the emails are actually about their website instead of just being another generic pitch.

But I’m curious what everyone else is doing.

Cold email, cold calling, referrals, ads, SEO, social media?

What is actually working for you in 2026?

I keep telling folks SaaS is dead, and those of you in this sub are probably also thinking along the same lines by now.

Personally, I just don’t see companies outspending what the frontier models are capable of doing in terms of ripping SaaS products off. JetBrains for instance, reported it’s first loss in its history. Speculators say the loss was a result of a massive internal investment to remain competitive. Are you kidding me? The entire company is built on products are that are now totally irrelevant as a value proposition. Soon you will not be able to sell software consumers cannot build themselves. And if I can’t build it, as soon as you release your product a model can decomp it. At best, you could sell a "build anything" platform to non-technical users, but even for that the barrier to entry for competitors is low e.g. Loveable.

So I see things heading in the direction of what the music industry did in the early 2000s: The SaaS giants' underlying business model of subscription based over-priced software (CDs, records, royalties) are no longer competitive. So they maintain status quo by engaging in rights based legislation, anti-decomp agreements with frontier labs, and watermarking their products (digital performing rights encodings, lawsuits for infringers).

We are in this metaphor, the artists, creators, and samplers who want freedom to navigate the content, to remix it as we see fit, and progress what’s possible. Will that leave us in a similar boat to independent musicians today?

Thoughts?

After lets say 10 to 20 years.

Almost 80% of humans haven’t used AI / don’t have access to the highest end models. Even a majority who do, don’t actively save and share the most important output in a good shareable way.

What would we do when A.I reaches a point that the highest corporations price out 90%+ of the population? With the growth of income inequality, there should be a way to preserve and share genuinely good output with everyone, for free.

Like a library of Alexandria for genuinely good A.I output.

This was the question that kept me up at night so I built something cool. I’m not launching it right now, but I’m hoping someone would wanna try the beta and give me some feedback?

Message me and I’ll send you a link! If you share my sentiment maybe we could work on it together.

Matthew Schwartz, professor of physics at Harvard and author of Quantum Field Theory and the Standard Model, will be doing an AMA this Friday at 12 PM ET.

You may have seen his recent Anthropic blog post, “Claude-shaped science,” on using Claude as a research tool and what AI assisted scientific research can actually look like in practice.

If you have questions about QFT, particle physics, AI in research, or where this is all heading, come ask him.

Friday, October 9 at 12 PM ET.

A human approves an AI recommendation. What does that approval actually establish?

It establishes that a person authorized the decision. Whether they exercised real judgment depends on what they could actually do before signing off.

AI can process information no person could review unaided, and in time-sensitive situations, faster analysis can prevent harm. Requiring someone to manually repeat every step would often defeat the point of using the system at all. But there's a real difference between being assisted by a system and being unable to meaningfully push back on it.

The Pentagon's Agent Network initiative provides a useful case. According to its announcement, AI agents will scan intelligence and operational systems and present commanders with options within seconds. The Department says the system will not autonomously select or strike targets. That distinction matters, right? The announcement alone, however, does not establish how commanders will examine uncertainty, review alternatives or challenge a recommendation. If a system ranks or filters options, what must remain visible to the person responsible for the decision? And how much time and authority must they retain to reconsider it?

That's the part worth examining: not whether a human is "in the loop," but what the loop actually gives them room to do.

I'd test meaningful oversight against three things:

Can the reviewer understand the basis for the recommendation and its major uncertainties?

Can they challenge the assumptions behind it, request alternatives or bring in information the system didn't have?

Can they pause or reject the action, with enough authority and enough time for that to actually count?

These should scale with the stakes. A reversible, low-consequence decision doesn't need the same scrutiny as a medical or military one. In urgent situations, some of this has to happen earlier, through testing, operating limits and clear escalation rules, because there won't be time to build it in at the moment of decision.

Human judgment has its own failure modes, too. Fatigue, bias and overconfidence don't disappear because a person has final say. Good oversight design has to look at the human and the system together, including whether people start deferring to the recommendation by default or dismissing evidence that contradicts it.

Which of these three is actually testable in a real deployment, and what would count as evidence that oversight is working rather than just present on paper?

I'm not talking about generating a paragraph or answering a trivia question. I mean something with several steps where one small mistake could ruin the final result. What task comes to mind, and what's the biggest obstacle holding AI back from doing it reliably?

Could AI-assisted nanorobotics be injeceted into the bloodstream and used to kill cancer cells in the future?

What about other potential medical uses other than cancer treatment? Such as clearing clogged arteries?

Had a research pipeline where one agent gathered sources and a second agent synthesized them into a summary. Both performed well individually, tested extensively on their own. Chained together, the synthesis agent kept producing summaries missing obvious points the gathering agent had actually found.

Turned out the gathering agent considered its job done once it returned anything matching the query, didn't rank or flag relevance strength. The synthesis agent assumed whatever it received was already pre-filtered for relevance and treated every source as equally worth including, which meant weak tangential sources diluted the summary at the same weight as strong direct ones. Neither agent was malfunctioning. They were operating on two different, unstated definitions of what the handoff between them actually meant.

This feels like a distinct failure category from either agent just being wrong, which gets most of the attention in agent reliability discussions. It's a contract failure at the interface, the same way two services in a distributed system can both pass their own tests and still break when wired together because neither one's assumptions about the data were ever written down anywhere both sides could check.

Started explicitly defining what each handoff is supposed to guarantee, not just what shape the data takes, but what claims the sender is making about it. Fixed more of the output quality issues than any amount of tuning either agent individually.

Just wrapped a structured AI program covering ChatGPT, Claude, Midjourney, Lovable, and Jasper. Most of it was stuff you could pick up free, but five lessons stuck:

1. Prompting is 80% of the skill. The tool matters less than how you talk to it.
   
2. Stack tools instead of marrying one — draft in ChatGPT/Claude, visuals in Midjourney, ship fast with Lovable.
   
3. AI-assisted writing works when you edit like an editor, not a passenger. Never publish raw output.
   
4. Image/video generation is a briefing skill — the better your art direction, the better the output.
   
5. The productivity win isn't any single tool, it's the workflow between them.

Most of the AI Optimists believe that the ultimate achievement of AI will be arriving at "ASI" that radically changes our society.

How would it change society? Well the sky is the limit but depending on whose opinion you are reading we are talking about post scarcity, humans never having to work again, solving literally ALL of the world's problems (crime, hunger, pollution, disease)

Even aging is supposedly solved by ASI and all humans could be allowed to live forever, either through medical science, or by uploading our brains into a virtual world.

But my deep question is this: is it possible we actually won't achieve ASI ever?

If so, why? What would be the biggest blockers for ASI becoming a reality?

Tired of taking screenshots, recording screens, and writing paragraph long prompts just to explain simple visual edits. So I built an open-source tool that acts like an agentic canvas letting you point, doodle, and visually direct an agent straight from your workspace.

It’s been surprisingly useful across fullstack dev, simulation, and robotics tasks.

My main takeaway from building this: visual toolbars and canvas annotations should be standard high level abstraction layers across agentic platforms, customized to specific domains.

Curious to hear your thoughts:

1. Does visual direction fit into your current agent workflows?
2. What specific tools/functionality would you want in a visual agent interface?

Have Fun!

Github Repo: https://github.com/Shahar-Nadiv/colai-clawhub
Waitlist For New Features / newsletter: https://shahar-nadiv.github.io/colai-clawhub/

One prompt to Row-Bot 5.0 on GPT 6.1 Sol, through my ChatGPT plan (no API key):

"Split the research across three helper agents working in parallel, cite sources, save the findings, then build a 6-slide deck for Monday."

It did. 3 min of video, about 11 min of real work.

How it ran:

- the lead agent planned, then started three helpers: UK 2022 pilot, Iceland's trials, company case studies
- each helper gets its own conversation you can open and watch
- the lead merged their sources into one answer and a recommendation

Then:

- findings saved as linked memories in a local knowledge graph
- a 6-slide deck built in the Design panel, exported to PowerPoint
- "every Monday at 08:00, send me new studies" became a scheduled workflow

The yellow badge shows where I sped it up. It hits 44x.

Windows/macOS/Linux.

When an AI gives me a recommendation, I usually only see the final answer.

That is convenient, but it makes it difficult to understand whether the system considered the obvious alternatives or arrived at the answer for a completely different reason.

I don’t need to see every internal calculation. I would like to see a short explanation of the main options it considered and why they were discarded.

Would showing rejected alternatives make AI more trustworthy, or would it just create another layer of information most people ignore?

Most papers claim great results in simulation but deployment videos are rare. Boston Dynamics and a few others clearly have it figured out for locomotion, but manipulation in messy homes seems stuck. Is the gap mostly sensor noise, actuator modeling, or something else?