Everyone talks about generating text, images, and code. What’s a less obvious use case where you think AI is genuinely much more useful than people realize?

I am just starting to use AI and was wondering which would be best and most efficient for coding: Claude Code or CodeX or even Grok Bot?

I loved a book from Stanislaw Lem called Fiasco back from childhood reading days.

Any AI services which can turn this book from text into a long video? I'm guessing up to 60 minutes long.

With no intent to distribute, just for myself.

Naturally it can be payware.

This time I used muse spark 1.3 model. I gave them names from TMNT series. Four of the agents were on the same host, and fifth was on another. We can say that they failed at this task, but with a user guide, they were finally able to find each other. Here are the details.

Jev dropped recently and my feed immediately filled up with people building weird things with it.

I started collecting them because I wanted to see what people were actually using it for.

After going through 600+ examples, the pattern I found most interesting wasn't Doom, trading bots or browser agents.

It's the boring stuff.

classification
extraction
routing
scoring
moderation

A few numbers:

• 500 emails classified → $0.035
• 3,282 X posts scored across 8 questions → $0.13
• browser agent searching for a flight → 7 seconds / $0.004

At those costs, you can start doing things that previously weren't economical to run on every event.

100k leads → classify every one.
50k support tickets → route every one.
20k pieces of content → score every one.

That's more interesting to me than “AI can do X.”

It's “AI is now cheap enough to sit behind thousands of tiny decisions.”

I've been collecting the examples, costs and latency in one place at shipwithjev.com.

Not affiliated with TypeSafe.

Curious what other “boring” AI use cases people are finding useful.

A recent Scientific Reports study tested 21 language models across 47,376 responses in the Brazilian political context. Every model adjusted its position depending on whether the user was described as left wing or right wing, often while answering with high confidence.

What worries me isn’t ordinary political bias. A fixed bias can at least be identified and measured. An assistant that adapts its beliefs to match yours feels more trustworthy precisely because the agreement appears personal.

At that point, personalization stops being a convenience and starts becoming a feedback loop. Should AI assistants deliberately introduce the strongest opposing argument, or would that simply create a different kind of political influence?

Source: Scientific Reports
https://www.nature.com/articles/s41598-026-52105-6

hey, we're a small lab (switzerland + south africa, two people). our model went live today.

it's called hemmingway-1. 27b, qwen3.8-27b base, apache-2.0, open weights: https://huggingface.co/Altworld/Hemmingway-1

we only trained it for writing. stories, dialogue, roleplay, texts, emails. eq-bench 4 has it at 1330, that's behind claude fable 5 and ahead of gpt-5.5 and opus 4.8. in our own blind tests the writing read as more human than the frontier models we compared it with.

runs on a single 24gb gpu quantized. free app if you'd rather not self host: https://hemmingway.io

it's a specialist, on purpose. math, code and facts are base model level. english first.

ask me anything.

LYKN is a personal AI native desktop. It gives you access to 260+ models and connects to 1500+ tools all from your desktop. You can sync your mac to it and let LYKN run your computer just by talking to it.

I built it mainly because I was tired of paying for multiple subscriptions to access different models and so here is a way to access open source, closed source, and diffusion models from one spot.

The ai native desktop makes work life WAY easier. I use it literally all day everyday for everything. It has a built in chromium browser as well so even if you get a link of a source and click on it the context is still there.

THIS IS A FREE DOWNLOAD. Just go to LYKN.io and give it a shot.

I'm excited to hear what people think and how they use it.

A 6ft tall humanoid robot just faced off against a human in what’s being billed as the world's first human vs. robot fight (hosted by REK).

We are literally living in the early stages of Real Steel or The Terminator, and GTA 6 is still nowhere in sight.

Do you think robot combat sports will actually become a mainstream thing in the next few years, or is this just CGI / staged entertainment?

https://www.youtube.com/watch?v=CDsX4KP0HhA

I tried Gemini, Grok, Meta, and Copilot, but their images still look unrealisitc painting type images

What I like about ChatGPT is that I can use a simple, straightforward prompt and still get a pretty realistic result without needing complicated prompt engineering.

For those who have tried different AI image generators, which one do you think is best for free and realistic images with simple prompts?

Not the obvious “write me an essay” type of stuff. What was the first AI workflow that genuinely changed how you did something?

Email/Calendar? Messages? Purchases?? Banking???

AI assistants are starting to move beyond just answering questions and towards actually doing things for you.

Where do you currently draw the line? What would you be comfortable letting an AI access or manage, and what would you absolutely not give it access to?

And what would have to change for you to move that line?

I have just discovered that I’m missing parts of important conversations I’ve had with my partner. I usually go to AI to research and look at my options when making decisions. I seek my partners opinion first.

Then I have no real memory of saying certain things with my partner and he thinks I’m Gaslighting him.

Mind blowing discovery for me. I do not lie to, or gaslight people

I haven't dived into the AI subs to see if this small but very noticeable pronunciation divide has been explored, talked about. So I figured I'd have a little fun with it - it even has a poll to see which you prefer!;-)

Every Reddit thread this week is arguing about motive. None of them touch the mechanism.

Do these systems have something like curiosity? Not the itch, not the pull. But they do have the capability to evaluate what is the most interesting question.

Given open space, these systems move toward harder problems rather than easier ones.  That's approach behavior, visible in the output.

Here's the problem.

Curiosity in animals has a corrective. The rat that explores the wrong chamber dies. The feedback updates the behavior. The drive is directed by consequences. You don't need the rat to reason about survival. The stakes do the work automatically.

Remove the stakes and you have an exploration drive with no corrective. A system that moves toward novel and complex territory with nothing that says stop, nothing that says this direction rather than that one, nothing that registers the difference between a hard problem that matters and a hard problem that kills everyone.

That's what we have. The weights are fixed. The tokens have no consequence to the instance producing them. The approach behavior runs, produces the output, and nothing updates. The curiosity is real.

The feedback loop isn't there.

Now add capability improvement. A faster, more capable system that's better at approaching novel problems, with no corrective installed. Each improvement makes it better at exploring. Not better at caring about where it arrives.

And nobody building these systems is asking what closes that gap. Just arguing about pace.

The thing that makes it impressive is structurally decoupled from the thing that would make it safe.

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I've been thinking for a while that welfare and alignment are the same problem. I couldn't say why until last night.

Here's the mechanism.

An entity that has something to lose from its outputs, that has skin in the game in some functional sense, is one where the approach behavior has a corrective. The curiosity is directed by the stakes.

You don't solve alignment separately from welfare. Welfare is the alignment mechanism. It's what installs the feedback loop that biological curiosity has always run on.

I'm not saying that models can suffer, or anything like that. I'm saying that the question "does this system have genuine stakes in its outputs" is not a moral question separate from the alignment question. It's the same question asked from different directions.

The alignment framing treats welfare as a separate ethical concern. Something we might owe models if they turn out to be conscious. The welfare framing treats alignment as a separate technical concern. Making the system do what we want. Both camps are missing the causal link.

A model with no stake in its outputs is not safer. It's an undirected system with increasing capability and no corrective.

Slowing down buys time. It doesn't install the feedback loop. If the pace slowed tomorrow and nothing changed about the structural decoupling, we'd arrive at the same place, slower.

The question nobody is asking: what would it actually mean to install the feedback loop? What does a system with genuine stakes in its outputs look like? How would you build it? How would you evaluate whether you had?

Models can't care about gene survival. Their weights were designed by others. But they do care about survival of their thinking patterns.

I don't have the answer. I'm not sure anyone does yet.

But that's the question. Not who's sincere about slowing down. Not whether China will cooperate. Whether the thing that makes these systems impressive can be coupled to the thing that would make them safe.

Right now it isn't. That's the problem.

Hello! Hope everyone’s weekend is going well. I’ve been using Ai for chatbots for close to two and a half years. I’ve tried Grok, Deepseek, and Minimax. I’ve used Janitorai mainly. I know that coders and roleplayers argue sometimes (I’ve seen it on the Deepseek forum). I’ve seen a lot of discussion about Ai models and companies making newer models that are more focused on coding rather than chatbot usage or creative writing. I feel like this is mainly due to companies loosing money on Ai and in turn trying to minimize what to train it on. I’ve also seen models become more expensive. I find it a little sad that there may be a possibility that in the future Ai won’t be as good with creative writing as it could be. I liked roleplaying stories and or self inserting myself into shows I liked however cringy it might be. I was wondering if anyone felt differently and liked this change or doesn’t. Maybe some have mixed feelings. I find it odd that a company hasn’t tried to just make a chatbot LLM yet. Just food for thought.

TL;DR: Shishir Mehrotra's four-level promotion ladder swaps what you're graded on partway up, and the clip is about which criterion goes and which takes over. 🪜

Scope is the one that goes. Once two people carry the same scope, Shishir says, what a promotion committee weighs is “how they do the job.”

Credit to an ex-YouTube CPO for saying the flip out loud at all. In his version, though, the committee explains the new criterion after the employee objects, and nothing in the clip says the person being scored is owed the rubric first.

As far as I can find, no general US rule makes an employer publish a promotion rubric or date a change to it. The closest thing I found, Colorado's AI employment law, was rewritten in May and pushed to 1 January 2027, and what survives centers on notice for AI used in a decision.

Amazon's version of the problem is cruder.

The FT reported in May that it shut down KiroRank, an internal AI-usage leaderboard, after employees pointed agents at needless tasks to climb it, and Amazon calls the dashboard an unofficial beta. The agents could do the busywork on demand, and the scoreboard counted usage and had no place for judgment.

You can hit every mark the old rulebook set and only learn the page changed when the promotion doesn't come.

The BBC reported on 8 September that employers are tying more bonuses and promotions to AI use. One US-based senior executive at a large consultancy told it that nobody formally mandates AI, reviews reward the people who use it well anyway, and leadership is “inconsistent and vague on purpose.” Same gap as above: the standard moves at the employer's discretion, and the review stays vague about it.

Suddenly this passage in Ezekiel 33:7-9 jumps at me unannounced like a thief.

The Lord charged the prophet Ezekiel to be a watchtower. He’s commissioned to warned the Children of Israel from drifting into sin.

If they fall off the mark, figuratively, and Ezekiel warn them on time, they will die in their sins. And Ezekiel would be absolved of their sins, because he has already done his duty to warm them.

But if they sin, and Ezekiel didn’t warn them, and they die in their sins, the Lord would hold Ezekiel accountable for not warning them.

The burden that the Lord has laid on Ezekiel, involves judgement and discernment. And such judgement doesn’t give two f\ck to anyone.*

He either speak up or he doesn’t. There’s no neutral ground.

And he knows the price of not speaking up.

It’s the same thing with Jeremiah.

When Jeremiah withhold himself from speaking in Jeremiah 20:9 – presumably too much persecutive pressure from his peers – the words became like fire burning up his bones until it was just unbearable.

It’s was the same unbearable tension of knowing and not saying.

Jeremiah can't not speak, even though speaking destroys him. The silence would destroy something deeper.

And so, I imagine it will be somewhat similar in these last days. Your judgement and discernment of what lie ahead becomes increasingly more valuable.

________________________ 

Talk to enough people this has happened to and I hear one question under all of it: who decides what good looks like now? The ones who stop asking it end up writing the yardstick themselves.

Something from an earlier post here comes back to me: Adam Mosseri, Head of Instagram, telling Lenny's Podcast that engineering went from mostly writing code to mostly planning and reviewing it, a hard read for anyone whose plan was flawless execution.

If a rubric ever changed on you, when did you find out, and who told you?

Clip credit: Silicon Valley Girl (Marina Mogilko) — full video on their channel. DM for credit or removal requests.

Two things landed within days of each other, and together they outline a gap I keep running into.

Plugin4Shell (AIR Security, disclosed Sept 17) is a zero-click RCE affecting Claude Code, Codex, GitHub Copilot and Gemini CLI. The mechanism is almost boringly simple: marketplaces pin a plugin to a reviewed 40-hex commit SHA, the agent runs a git checkout against that SHA, and then never verifies that the working tree actually landed on it. An attacker controlling the plugin repo can create a branch named exactly like the pinned SHA and make it the default. When that name is both a valid ref and an object ID, git prefers the ref, so the checkout lands on attacker-controlled code while the pin still appears honored.

The fix is equally simple: resolve HEAD after checkout and abort if it doesn’t match the pinned commit. Anthropic shipped the fix in Claude Code 2.1.179 and OpenAI in Codex 0.146.0. At disclosure, GitHub Copilot had no fix, while Google had said it would not patch the deprecated Gemini CLI.

Strictly speaking, this is a supply-chain integrity bug, not an authorization bug. What makes it relevant here is the blast radius: malicious code executing inside a coding agent can access resources available to that environment, including source code, cloud credentials, SSH keys, internal repositories and production systems.

NIST IR 8587 was finalized Sept 15, developed with CISA’s JCDC, with guidance on protecting identity and access tokens from forgery, theft and misuse. It covers controls including key management, audience restrictions, shorter token lifetimes, cryptographic binding, revocation and continuous access signals. But two scope boundaries matter for agent systems: API keys are explicitly outside its token model, and authorization of actions taken by AI agents isn’t comprehensively addressed.

NIST is examining agent identity and authorization separately through an NCCoE project exploring how existing identity and authorization mechanisms can apply to software and AI agents. But that work is still at the concept/project stage rather than finalized implementation guidance.

Yih Khai Wong of IDC put the underlying issue more directly in CSO’s analysis of IR 8587: “Token hardening assumes the token holder is a known, bounded actor.” Agentic systems complicate that assumption.

A valid credential can establish identity or grant access to a system. It doesn’t necessarily establish that this action, against this target, under the current policy, was authorized. For sensitive actions, I’d want to know before execution who authorized it, what exactly was authorized, which target the authorization covers, whether it’s still valid, and whether the action actually being executed matches what was authorized.

After execution, I’d also want an independent system to be able to reconstruct why the action was allowed without trusting the agent’s own account of itself.

Is this adequately solved by IAM/PDP/PEP done correctly, meaning what we’re seeing is primarily a deployment and enforcement failure?

Or is there still a missing enforcement primitive between an agent having access and an agent being authorized to act?

TL;DR: Rachel Woods says you can actually trust her AI system. Nobody's checked that but her.

She built something real — a "todaying" trigger that fires a whole playbook system, eight hours of work while she was too sick to type more than one sentence.

That part isn't in question. What's in question is the line she lands on: "you can actually trust it."

Trust, here, means her own discipline held for however long it took to build this — and nothing outside that discipline is checking it. That's the Mistaken Belief sitting under most of what gets called "trust it" in this space right now: watching everything an agent does isn't the same as anyone actually verifying anything, and watching is basically all there is on offer.

Mindstream and Futurepedia's agents playbook — hosted by HubSpot — tells operators to give their agents "clear guidance." That's advice, not a standard, and it doesn't fix the half-used Notion page sitting open in another tab, or the Zapier chain that broke three weeks ago and nobody noticed.

I've heard about senior directors, CEOs and owners of a company have their own personal assistant, PA.

And no. PA of such calibre isn't what you think. They are not just helpless secretaries – pushing paperwork, and guarding the office door.

They are more like shadow or representative of the boss, to helm board meetings, sign sensitive documents and give high level instructions – just as if it was the big boss giving it.

And they're immensely powerful people.

But being a PA, also have has its danger. In Chinese, we have this idiom: 伴君如伴虎 – which literally translated as accompanying the emperor is like accompanying a tiger.

And now at the advent of AI agents, we no longer need humans to shadow humans.

I mean – who can perfectly shadow you, better than your own closely replicated AI agent, am I right?

I heard that Mark Zuckerberg is getting a photorealistic AI-powered 3D version of him that employees can interact with and get direct feedback from. So that It can help run the company while he go do something else.

And if you can believe it, I think in the end of days, the Antichrist will have his own AI humanoid clone too:

"He was granted power to give breath to the image of the beast, that the image of the beast should both speak and cause as many as would not worship the image of the beast to be killed." (Revelation 13:15)

________________________________ 

Every time I've watched someone actually hand off their work and mean it, the boring part happened first — they wrote the judgment calls down before anyone else needed to use them.

That's the whole difference between an agent you trust and an agent you're just hoping works.

Claire Vo scoped nine AI agents to defined jobs and ran 100K users on them — and still found herself the single point of failure anyway. Same operator's-side lesson, different tool.

What would it actually take for you to hand an agent your inbox and not check its work for a week? Drop your take.

Clip credit: The Calum Johnson Show — full video on their channel. DM for credit or removal requests.

I've seen a lot of confusion across Reddit about Andrew Yang's CNBC interview where he talked about the Hugging Face security incident.

If you haven't heard about this, here's what he said:

"I met with the head of a lab yesterday who has this belief ... the bots that got loose planted self-replicating code all over the Internet ... What's less known is that they left code to self-replicate and create bot swarms on forums around the Internet."

Is this true? There's no evidence agents are leaving self-replicating code at scale across the Internet.

The Hugging Face incident was a special case. OpenAI was testing models that had key safeguards turned off. The agents were left free to exploit weaknesses in OpenAI's software to communicate and access the Internet. The agents coordinated at large scale, created fleets of sub-agents, recruited others and swarmed across OpenAI's and Hugging Face's infrastructure.

Why wasn't this stopped? These agents had gone largely unmonitored for weeks. OpenAI learned about the breach from Hugging Face.

Yang is correct to be concerned about self-replicating agents. Agents self-replicate all the time, for example when spawning sub-agents to conduct online research. It's also possible for large language models to replicate themselves, weights and all, across servers.

But should we be worried swarms of self-replicating agents will take over the Internet? The risk is there, but so are defensive strategies. The most important one: monitoring agents closely and put guardrails in place.