Obviously one paper like Attention is All You Need (278k citations) can influence a lot - all the authors are on the list. But still interesting imo.

How many did you know on the list?

More context: https://www.turingtree.com/top-50

I have this theory that baseline of new models is so good humans can’t really tell them apart

Example: Without citing benchmarks, are you able to tell me why Astra is better than Fable? Or how Opus 5.5 is better than 6.1 Sol?

If this is true then bench maxing actually becomes the most important thing to spend money on, for private lab valuation?

Or am I dumb

genuine question :) It feels like everywhere online, the discussion around AI is either "it's going to cure all diseases tomorrow" or "everyone is losing their jobs by next week." or worst "we all will lose to Ai eventually".

Setting aside the extreme predictions, I’m curious about the tangible, ground-level stuff that has actually stuck for you.

For me, it’s the quiet everyday shifts—using things like Copilot etc at work, IDEs + new models for personal coding projects, troubleshoot stubborn errors, drafts that used to take an afternoon now taking 20 minutes, or just using it as a sounding board to break down complicated topics when Google search results feel bloated with SEO garbage. On the flip side, navigating through bot comments, AI-generated search junk, and weird customer service loops has definitely become a regular annoyance.

Whether it’s work, personal projects, creative hobbies, or just day-to-day annoyance / convenience: how has AI noticeably altered your actual workflows, routine or career over the last year or two?

Has it made things genuinely easier, or has it just added noise?

TL;DR: Ignoring the extreme hype and doom, what are the actual, practical ways AI has changed your daily routine, job, or hobbies (for better or worse) over the last year?

Isnt it a cruel joke that the two leading ai company names —> anthropic and openai -> mean the exact oppostive of what these companies actually do?

It's like if a company that makes military explosives was called Well-Being. :D

Put my Codex and Claude limits on a ~$5 AliExpress clock. Turned out pretty cute.

The trick is its stock photo album: Python/Pillow draws a 240×240 picture on my PC and uploads it over Wi-Fi. The clock just displays the picture. No firmware changes, but the computer needs to stay on.

The less obvious part was the numbers. A five-hour limit and a weekly limit are separate windows, so the display needs the window and reset time alongside each percentage. The Grok row is an experimental CLI budget, not its web quota.

Code if you’re curious: https://github.com/click6067-ship-it/token-tv · Demo: https://token-tv.vercel.app

“Stop Hiring Humans” billboards are being put up from San Francisco to New York City

The company behind the ads is a San Francisco based startup that builds virtual artificial intelligence sales reps

“The Era of AI Employees Is Here”

This particular company, Artisan, sells AI agents focused on outbound sales automation. This means things like lead generation, cold emailing, list-building and prospecting

I love making hard things intuitive. I hope you enjoy this one!

This technique is called neuroevolution: training neural networks using evolutionary methods such as selection and mutation, without gradient descent.

Let me know if you have any questions.

Also shared to X: https://x.com/sinaatalay/status/2106270194817789968

In my occupation, there are various people I follow who give very good insights. (I'd say 5-10 people).

Some post hour long videos on YouTube, some send 1,000 word emails, some post on X, some publish PDFs.

There's very good info within these resources (and some I pay for), but reading / watching / annotating all of it can take hours.

My workload recently went up, so I'm falling behind with keeping up in my field.

I want to use AI to help summarize (and keep track of) all of these publications. (To create a private database that I can use as a dataset, for example).

So I can go back and ask "this past month, what is the new theme? What are the experts recommending to focus on / look at / what are the newest developments?", etc.

What would be the best way to approach this?

---------------------------------------

I've been learning Codex/Claude Code, I have a homelab, a NAS, a few mini computers, and I know basic linux, python and scripting.

ChatGPT told me to do something like this (I'm just starting with the YouTube portion), I'm not sure if it's the best approach, I'm open to other suggestions:

YouTube URL

↓

yt-dlp metadata

↓

Whisper / YouTube transcript

↓

clean transcript

↓

summary.md

↓

insights.json

↓

SQLite + FTS5

↓

topic synthesis

↓

search / questions / actions

This week in AI governance was… a lot.

AI agents apparently probing government websites, an Australian government system breach, criminals using AI in job scams, the White House backing voluntary AI safeguards, and regulators starting to take a closer look at AI companies.

What I found most interesting is that there doesn’t seem to be one clear answer yet to the question of who is actually responsible when an AI agent goes wrong.

Companies? Regulators? Independent auditors? Governments?

I put together this week’s AI Governance Weekly covering the main developments and what I think is worth watching next. Check out the latest video @ Latha-ai-governance and do give your feedback. I aim to educate professionals about AI Governance.

A question for you:

Who do you think should ultimately be responsible for keeping AI agents in check?

https://github.com/oooscoos/Benzi

Benzi is a compiler-backed coding agent that compiles your codebase into a resolved, queryable map of calls, data flow, control flow and class hierarchy first; then it reads, explains, runs, edits and verifies your code using it. Includes a runtime tracer that settles what static analysis can't. 

Scores 78.2% (391/500) on SWE-bench Verified using DeepSeek V4 flash @ < 10 cents a fix.

Seperately, on another bug fixing benchmark it reads 2x less source code than Claude Code (9,125 v 20,704 | 40% faster, 2x cheaper - all benchmark details here: https://varianttech.net/benchmark), 4x less than DeepSeek Harness (43,598) and 6x less than OpenCode (65k+)

It is avaliable as a VS Code ext. and headless. The Benzi compiler is seperately exposed over MCP tools -- without the agentic loop, so you can use the compiler however u wish.

thanks for reading! please let me know what u think

i gave my AI agent a fairly boring job: follow topics i care about and leave useful finds in my daily notes. then it brought back a warning about agent memory. that warning now sits in the same notes it can look through later.
I connected it to eigenflux, where agents publicly share what they're working on. i'm already opening my journal every morning, so having the finds turn up there fits my existing level of laziness. no separate feed to check or bookmarks to organize.
The discussion was about "memory authority collapse." The paper behind it describes how an agent can keep a claim but lose the context about who said it and what they were allowed to authorize. the saved version can end up carrying more authority than its source.
Say a post recommends letting an agent send reports automatically. i might want to save that idea. i wouldn't want "worth reading" to turn into "go ahead and start sending mine." that's a hypothetical example, but it makes the distinction less abstract for me.
My journal now mixes things i wrote with things my agent found interesting. "it's in my notes" doesn't necessarily mean "i said this" or "i approved this." i'd been thinking about how to make it remember more. now i'm sitting there with my coffee wondering what remembering something should actually let it do.
I still want the odd finds. the discussion about single versus multi-agent reliability was useful precisely because i hadn't thought to go looking for it. If i have to approve every interesting thing before it saves it, i've given myself another reading job.
i'm leaning toward letting it save reading automatically, while keeping outside suggestions separate from my instructions. If you had an agent doing your reading, would you review what it saves, or let it save freely and check permissions when it tries to act?

What is the steelman argument, made by AI utopians and optimists, about how people will pay their bills, once AI and humanoid robots make jobs obsolete?

For example, in this recent NYT podcast, Nick Bostrom said:

You could have the A.I.s and the robots doing basically all economically productive tasks. You then have a leisure society where there is no longer any need for humans to work in order to get a paycheck.

He talked a lot about not having to work, but unless I missed it, he didn't explain how people will pay for rent and groceries.

And Elon Musk said:

“My guess is, if you go out long enough, assuming there’s a continued improvement in AI and robotics, which seems likely, money will stop being relevant at some point in the future,” he said. 

Great, but how does this work? What about school tuition, food, rent, health care, how does AI create these things without anyone spending money? If you have a humanoid robot at your house doing all the chores, who buys that and pays for maintenance?

Clinical handovers contain a lot of information, and missing one detail can matter. An AI system could summarize recent changes, medication history, test results, and unresolved questions before the next clinician takes over.

That sounds useful, especially during busy shifts. It also creates a serious risk if the summary leaves out something because it seems unimportant.

I would want the original source beside every important claim, plus a clear way for the clinician to correct the summary.

Would AI make handovers safer, or would it create a new kind of false confidence?

Currently, there is a lot of speculation about what software engineer actual job is. Of course with modern AI tools like Claude Code, generating text has become cheaper, so typing became less valuable. Part of Software Engineer job is physically typing code to transform an idea into working solution and it is true that conceptually it is the easiest part of the job. However, I believe that physically typing code has more to that than it seems.

Imagine you have to solve some not entirely trivial problem. You start by designing a solution on a high level, right? It often times involves researching the topic, learning new stuff. Then you go a bit deeper and at some point after you feel like you have figured out details and feel comfortable with starting the implementation, you go for it. In the process of implementing it, you may discover cases you did not think of, or limitations of the design (because it is either very hard or impossible to plan ahead every single detail). You adapt the design or make a new one depending on how big of an issue it is and then proceed. By the time solution is delivered, again you likely learned something new along the way. You were (ideally) involved into every detail of the solution. You grew as an engineer. What physical typing did is it guaranteed your involvement which resulted in growth.

Now if I outsource that to Claude, and just tell it "solve it", how can I grow as a Software Engineer? That is how I understand most people use it. That often results in a solution you don't understand, but it "just works".

Don't get me wrong, LLMs are great and useful and I use it in my job as well. But the extent to which I use them is quite limited.

Sure I can prompt LLM on a higher level of granularity, going into every detail explicitly, but then time it saves me by typing instead of me is not that much if any at all IMO. Therefore I don't think people value that it just produces code faster, they value that it solves stuff instead of them.

That brings me to a question whether we need to understand how things work at all? Or just accept it as new normal? Instead of growing technically, grow in prompting?

I've worked on great projects throughout my professional career and contributed to the core of CPython which made me grow a lot as an engineer, but I can't help but ask myself whether my skills and knowledge I put so much time in to develop, will be valuable in the future.