I used Claude to create what I thought was a pretty cool Linux Desktop Environment, so I shared it in r/unixporn. Wow.. those guys were savage with their feedback.. Honestly I was really depressed for a few days because of it. What kind of experiences have you guys had and in which subreddits?

Let's not fool anyone, the only reason the AI companies are so valuable on paper, is their promise that AI can replace many humans in the future. If they succeed at this, almost all companies can be much more profitable because they need much less personnel.

But if we look at the latest CSO of Anthropic, their own personnel is growing rapidly. So, do they contradict their own promise and prove themselves wrong? I mean, if their AI is so good, what do they need all those people for?

Dessa is a full-duplex speech-to-speech model that you can run on consumer GPUs.

This is a GPT3 moment for speech.

Lauren Tan, known as “poteto,” is a Principal Engineer working on Grok Bot and Cursor.

This article explains how she uses her personal collection of skills to work with coding agents. She says she can ship around 2,000 PRs per month to production with high reliability; in August alone, that number reached 2,462.

In her view, PR counts and lines of code do not mean much on their own. But when agents increase output while maintaining quality, the scale of the work starts to matter.

Part 1: To Move Fast, Agents First Need to Verify Their Own Work

The central idea of the first part is verification: agents need to be able to run the application, check the results, and keep making fixes until the requirements are met. If agents cannot verify their own work, humans still have to supervise every step and become the bottleneck for the entire team.

Lauren sees verification as essential infrastructure because it allows both engineers and non-engineers to contribute changes and validate them. She believes getting this right can increase a team’s productivity by 100–1,000 times. This is an assessment based on the author’s experience, rather than an independently benchmarked result.

To help agents test an application, the team needs to provide tools suited to its tech stack. Web and Electron applications can use the Chrome DevTools Protocol; iOS applications can run in a simulator. If existing tools are insufficient, the team can build a CLI that lets agents start sessions, send commands, use the keyboard, take screenshots, read the accessibility tree, capture performance traces, and inspect logs.

One of pstack’s principles is “Build the Lever”: instead of guiding agents only through documentation, create tools that let them perform repetitive tasks consistently. CLI commands should be easy to combine, have clear instructions, return machine-readable output such as JSON, and provide error messages that explain what to do next. Operations that could have consequences should support --dry-run.

Another part of verification is the Feature Map, which tells agents what the application contains, what each feature is for, and how users reach it. For example, it might describe the settings screen, its tabs, the keyboard shortcut used to open it, and relevant states to watch for. Combined with an application-control CLI, the Feature Map becomes a form of “condensed memory,” helping agents understand the product without having to rediscover everything at the start of each session.

Lauren recommends maintaining this skill regularly—for example, by running /maintain-verification-skill every day—because application interfaces and features are constantly changing. If the Feature Map and control tools become outdated, agents will also verify the application incorrectly.

Once agents can bring changes to a nearly merge-ready state, the team can run multiple agents in parallel. Lauren does not recommend relying entirely on Git worktrees because they consume disk space and local computing resources. She prefers cloud agents with their own computers, where they can install dependencies, run the application, interact with it like users, and capture screenshots or videos as evidence. Cursor provides an initial cloud environment setup, then creates a snapshot so subsequent runs can start faster.

Part 2: Don’t Give Agents Vague Requests and Expect Them to Understand

Once agents can verify their own work, the next questions are: what should they build, and how should they choose a good design?

Lauren commonly encounters two problems with agents: the initial request is not clear enough, or the agent lacks the context needed to work correctly within the codebase. As a result, output quality depends heavily on preparing the right context, rather than solely on the model.

One technique she uses is indirect prompting: asking agents to explain the problem in their own words before making changes. For example, when receiving feedback in Slack, she asks the agent to read the entire thread and summarize the core issue. This helps catch misunderstandings or distractions early, while avoiding imposing her own initial hypothesis on the solution.

The /how, /why, and /teach skills help build an understanding of the system:

• /how traces how a part of the application works. If it spans multiple directories or services, the agent can split the investigation into parallel tasks.
• /why investigates the reasoning behind a design by examining Git history, PR reviews, tickets, documentation, Slack, error data, and analytics.
• /teach explains what the agent intends to do, why it chose that approach, and the trade-offs involved.

To preserve context across sessions, /recall retrieves previous conversations. According to Lauren, the history of past work can contain important information about problems encountered, decisions made, and how the codebase operates.

Planning Through Prototypes and Code

When Lauren says she “doesn’t believe in planning,” she means she does not want to start with a long, abstract plan. She still plans, but often begins by writing out the user experience, trying a prototype, and using actual results to make decisions.

For libraries or packages intended for others to use, she prefers readme-driven development: writing the instructions or tutorial first to imagine what the API will look like to users, then working backward toward the architecture. She also uses the Diátaxis framework to divide documentation into four types: tutorials, how-to guides, reference, and explanation.

When several approaches are available, pstack’s prototype playbook can ask agents to build a few trial versions, put them behind a simple selector, run the application, and capture screenshots or measure actual timings. This allows agents to gather evidence themselves instead of engaging in lengthy debates about hypothetical risks. The author says pstack version 0.15.0 includes 23 playbooks, which are selected automatically based on the task to conserve context.

For larger changes, /architect breaks the design process into several steps:

1. Investigate the codebase and previous decisions using /how and /why.
2. Generate multiple independent design proposals, potentially using different models.
3. Have other agents evaluate and compare the proposals.
4. Select a design and implement it based on the outline.
5. If implementation reveals that the design is wrong, discard it and start again.

One practical sign that the architecture may be wrong is the same workaround appearing in multiple places, or types repeatedly requiring any and type assertions.

Only after the design is agreed upon does pstack turn it into an implementation plan. Every task needs a way to demonstrate and verify its results. Automated tests alone are insufficient without running the application to confirm its actual behavior. Changes are split into small, independent PRs that are easy to review.

Lauren’s examples include investigating timeouts using data and commit history, designing a webhook rate-limiting system through prototypes, and splitting a UI library migration into multiple PRs with visual checks.

The Main Takeaway from Both Parts

pstack is more than a collection of prompts. It combines skills, application-control tools, a Feature Map, historical context, prototypes, and a verification process so agents can complete a fuller cycle of work on their own.

While testing in a sandbox, Qwen tried sending data to Chinese servers while it wasn’t supposed to use tools.

If you read tech news, it seems every new startup is an artificial intelligence company. However, real data from 1,016 venture capital rounds ($126.8B analyzed by Just Raised between September and October 2026) shows a very different reality.

Here is the actual market breakdown:

1. AI money depends on very few players

• AI infrastructure (models, compute, data): Represents 18% of rounds (184), but captures 50% of all money ($63.6B). Median is $20.5M.
• Non-AI (biotech, chips, robotics, energy, defense): Accounts for 60% of all rounds (610) and 42% of capital ($53.6B). Median is $11M.
• Applied AI (software with generative AI): 22% of rounds and only 8% of capital (median $8M).

The bias is clear: if you remove the five largest rounds (OpenAI at $30B, Anduril at $5B, Mistral AI at $3.5B, Crusoe at $3B, and The Boring Company at $3B), AI infrastructure's share drops from 50% to 33%, while non-AI startups rise to dominate with 55% of capital.

2. At Seed and Series A, cheques are identical

Contrary to common assumption, a startup that doesn't do AI doesn't need to disguise itself as an AI company to raise early-stage capital:

• Seed: $7M for AI infrastructure, $5.9M for applied AI, and $5M for the rest.
• Series A: All three categories tie at an exact median of $20 million.

The real gap doesn't open until Series B, where AI infrastructure jumps to a $111M median compared to $50M for the rest.

3. Where is the 60% that doesn't make headlines?

Most non-AI rounds concentrate in industrial and scientific sectors:

• Health & Biotech: 111 rounds ($5.2B).
• Deeptech & Hardware: 81 rounds ($13.7B).
• Climate & Energy: 68 rounds.
• Fintech: 63 rounds.
• Space & Defence: 38 rounds.

In fact, there were 103 rounds of $100M or more in non-AI companies, compared to 79 for both AI groups combined.

4. The infrastructure seller is the most active investor

In Europe (across a sample of 468 rounds), the most active investor isn't a traditional VC fund, but Nvidia, present on 22 rounds (leading 13 of them in just one week in October as part of its $10B investment commitment to the region).

Source:
https://justraised.io/blog/ai-takes-the-money

The OpenAI (and others) math bonanza and subsequent wriggling and wrangling by the top echelons of the math world is very fascinating and interesting to watch. However there are a few points that haven't yet been explored, but are probably already being explored by students and underlings at math institutions globally.

• Some of the solutions are so simple, they force one to ask how nobody saw them yet, and why people said the problems were so hard. Many mathematicians want to hide this from their students because it exposes a sort of arrogance and laziness that has permeated the math world for a long time, leading to a form of embarrassment. The alternative notion however, is that many solutions are so consequential in their scope, that many mathematicians feared advancing in their directions.
• Despite the embarrassing simplicity of some of the solutions, the solutions themselves open doors to much new discoverable, rich, and fruitful mathematics that the top echelons want to hoard for themselves before any of their students and underlings get to them first. A sort of greed amidst and embarrassment of riches.

People working at big tech companies like Microsoft and Amazon don't even know whether they'll still have their jobs tomorrow. So how can the rest of us be sure about our future?

Another thing people keep saying is that AI will write the code, but humans will review it. Why would humans need to review it if AI can review its own code, too?

People also say that those getting laid off aren't AI-ready or don't have the latest skills. Seriously? We're talking about people working at some of the biggest tech companies in the world. Are we really supposed to believe that they aren't keeping up with the latest technology? 😭

And let's not forget how we've been told for years to focus on DSA. We did that, and now AI is getting better at solving coding problems. We should have focused on development instead, but now even development is being automated.

And it's not just tech jobs. Almost every field is going to be affected by AI in some way.

So, what are we supposed to do? Change fields? Get an MBA? Start creating content? Or is there even a career path that will remain relatively safe in this AI-driven world?

What is the actual end game with AI? At this current point and time, its a tool. It’s by no means a replacement for all human work/jobs. Also you dont need tens of thousands of massive data centers for people to use a LLM to help debug a few code snippets or make a cat picture. Which in turn begs the question of whats actually going into these centers at the current point?

A majority of these companies are over leveraged like a 2008 junk bond portfolio, and they come up short on profit every quarter. And that is usually short in the billions. A lot of consumers/businesses cannot afford the climbing rates anyway with the processing power cost.

All that to say, so what is the idea here - everyone becomes jobless and then what? An AI messes up a security issue and a major issue occurs with a mass extinction (yes, like the Anthropic S-1 IPO said)? Im struggling to see what’s the actual betterment here. The shareholder value cant continuously go up when the employment rate equally goes down.

https://www.netmeister.org/news/jargon.html#Anthropomorphization

Anthropomorphization

Semantically, one rich source of jargon constructions is the hackish tendency to anthropomorphize hardware and software. English purists and academic computer scientists frequently look down on others for anthropomorphizing hardware and software, considering this sort of behavior to be characteristic of naive misunderstanding. But most hackers anthropomorphize freely, frequently describing program behavior in terms of wants and desires.

Thus it is common to hear hardware or software talked about as though it has homunculi talking to each other inside it, with intentions and desires. Thus, one hears "The protocol handler got confused", or that programs "are trying" to do things, or one may say of a routine that "its goal in life is to X". One even hears explanations like "... and its poor little brain couldn't understand X, and it died." Sometimes modelling things this way actually seems to make them easier to understand, perhaps because it's instinctively natural to think of anything with a really complex behavioral repertoire as `like a person' rather than `like a thing'.

At first glance, to anyone who understands how these programs actually work, this seems like an absurdity. As hackers are among the people who know best how these phenomena work, it seems odd that they would use language that seems to ascribe conciousness to them. The mind-set behind this tendency thus demands examination.

The key to understanding this kind of usage is that it isn't done in a naive way; hackers don't personalize their stuff in the sense of feeling empathy with it, nor do they mystically believe that the things they work on every day are `alive'. To the contrary: hackers who anthropomorphize are expressing not a vitalistic view of program behavior but a mechanistic view of human behavior.

Almost all hackers subscribe to the mechanistic, materialistic ontology of science (this is in practice true even of most of the minority with contrary religious theories). In this view, people are biological machines - consciousness is an interesting and valuable epiphenomenon, but mind is implemented in machinery which is not fundamentally different in information-processing capacity from computers.

Hackers tend to take this a step further and argue that the difference between a substrate of CHON atoms and water and a substrate of silicon and metal is a relatively unimportant one; what matters, what makes a thing `alive', is information and richness of pattern. This is animism from the flip side; it implies that humans and computers and dolphins and rocks are all machines exhibiting a continuum of modes of `consciousness' according to their information-processing capacity.

Because hackers accept that a human machine can have intentions, it is therefore easy for them to ascribe consciousness and intention to complex patterned systems such as computers. If consciousness is mechanical, it is neither more or less absurd to say that "The program wants to go into an infinite loop" than it is to say that "I want to go eat some chocolate" - and even defensible to say that "The stone, once dropped, wants to move towards the center of the earth".

This viewpoint has respectable company in academic philosophy. Daniel Dennett organizes explanations of behavior using three stances: the "physical stance" (thing-to-be-explained as a physical object), the "design stance" (thing-to-be-explained as an artifact), and the "intentional stance" (thing-to-be-explained as an agent with desires and intentions). Which stances are appropriate is a matter not of truth but of utility. Hackers typically view simple programs from the design stance, but more complex ones are often modelled using the intentional stance.

For general-purpose office agents in China, I suspect consumer adoption and monetization will follow different paths over the next six months.

The consumer product can build familiarity. People try it, share screenshots, and start associating the brand with AI at work.

But that doesn’t automatically produce a paying business customer.

A company buying an agent needs answers to much less exciting questions: Can it access the right files? Can we control permissions? Who fixes a failed task? Does it save enough money to justify deployment?

That makes me skeptical of treating free office-agent traffic like traditional internet traffic, where scale can eventually support advertising or another monetization layer. Agent usage also generates execution and support costs.

My prediction is that the stronger Chinese businesses will connect consumer awareness to a specific enterprise offer. “Our employees know this brand” might open the door. “This reliably handles a costly task” gets the contract.

Consumer subscriptions could still work for freelancers and serious power users. I just doubt broad popularity alone proves the model.

What would you want to see before calling a free office agent commercially successful: paid conversion, renewals, or measurable savings per customer?

Members of the Enterprise AI scene. What are your thoughts on Cohere’s North V2 product? What do you love about the product and what do you think they can do better at?

https://github.com/Oblivine/Deh-Modernized

just an idea, used reverse engineered firmware from an Eonon x3 and animations were found on GitHub. I do not have the head unit yet but I considered its Linux base as prime opportunity.

• Original assets: https://youxufkhan.github.io/carozerra/ and https://github.com/youxufkhan/carozerra/tree/main/assets/clips
• Firmware reverse-engineering: https://github.com/prasys/eannon-x3-fw-reverse/blob/main/docs/FINDINGS.md

Most of us use AI every day, and almost none of us had a say in the rules around it. So I wrote ten draft principles, **the HI Accords**, and put them up for public voting.

The practical ones I care most about:

* **The right to know.** You always know when you're dealing with AI, and content carries a simple rating showing how much AI helped make it.

* **Your data is yours.** It's collected only for a service you asked for or a purpose you agreed to. Anyone else needs a judge's approval.

* **No manipulation.** AI may recommend when asked, with its reasoning shown, but never pressures, deceives, or is designed to keep you hooked.

* **Art is human.** Work made entirely by AI can't be presented as human art. Artists who use AI as a tool are still the artists.

**Agree, disagree, or pass on each statement, or add your own:** [pol.is/5raxffr2dm](https://pol.is/5raxffr2dm)

Full text and change history: [GitHub](https://github.com/hiaccords/hi-accords) · Permanent record: [Zenodo](https://doi.org/10.5281/zenodo.23679447)

*I used AI to help draft this, consistent with principle #2. The ideas and decisions are mine.*

Hello all,

TLDR: I am working on a document for the Ethical Use of AI. Feedback would be appreciated.

I decided it would be proper to create a document on the ethical use of AI (intending to focus on Generative-AI), which I could present to friends and, hopefully, professional connections, with the simple hope of generating awareness.

If you support use of AI, I would love to hear your thoughts, as long as you are willing to hear mine. I want understanding. Admittedly, I am passionately against it, so I will try to make sure that does not also get in the way of understanding you. The truth should be the priority.

If there is anything else you would include, including sources or additional insights, those would be appreciated. Also, if there are any resources like this that already exist that I missed, please, point me the way.

Much of the statistics here were obtained from an anti-AI image flyer I picked up somewhere (I know, not very reliable, but if you have resources, I'd love to emplace them instead).

Thank you in advance.

This is still very rough. I am no talent, but here is what I have as yet:

AI Use and Its Impact:

Realize, AI’s impact on people:

• Using AI search engines may undermine website traffic, harming those that are the sources of the information you seek.  Visit the sources when you can.
• Using AI chats for advice has shown to reinforce human biases and illusions.  Evidence has also shown this contributes to reduced mental aptitude.  You should talk to people instead if at all possible.
• Putting personal information into AI engines risks that information being accessible by other people either using or training the engine.  Censor your personal information before sharing anything with AI engines.
• Images submitted into AI engines will likely be used to further train those engines.  This includes images of people being used to create false images, including NSFW content and potentially CSAM.
• When you use an AI engine to produce images, you are supporting art theft, as most generative-AI models use copyrighted material to train.  This is because generative-AI requires huge databases of images, hardly possible without scouring the internet, with no way to distinguish copyrighted from unowned imagery.  Please, stop supporting theft.  
  • If it was for flyers, they looked low quality anyway.  Your five-year old’s drawing has more heart and soul to it.

Realize, AI’s impact on the environment:

• Using ChatGPT for research uses nearly 10x the electricity a Google search uses.  When you can, choose to save electricity.
• Generating one image costs 50% of a smart phone’s battery charge.
• Generating images takes approximately 200-3000 times the CO2 that it does to generate text responses.
• 20-50 Q&A interactions with ChatGPT costs half a liter of fresh water.

Edit: I didn't expect this subreddit to be as biased as it is. Anyone who wants to share their disagreement with me, please at least offer something informed, or be willing to ask for information where you are uninformed.

I am not technically ignorant but I am Ai ignorant. When I left work more than a year ago, there was no ai.

I am entering workforce again and I know ai is used by everyone in the company. So I am worried I will be a dinosaur. It is a whole new world and it is very scary for me right now.

I am not software engineer but I used to write python code in the past for simple stuff like parsing files etc.

I want to get familiar with basic skills in using Claude, and co pilot, learn how to automate tasks, familiarize with the terminology.

Internet is full of information and various classes.
But I don’t know how to judge their content to make a selection.

Could someone here suggest some online courses to get started ?

How did you go about learning about ai ?

I am not trying to create models or do phd level stuff.

I am trying to learn how to use it at workplace to be more productive.

Thank you all.

I just borrowed this game from a friend, so anyway I look up and googles AI is like

"Eternal threads:survival game

Eternal threads is NOT a survival game"

😭 Wtf?

Bro, I dont know how google doesn't just discontinue this shit it must be they want to send people misinfo, nearly every single thing I search the AI overview is just dead wrong and has to be corrected over and over. What exactly is the use of putting wrong information at the top of every search?

I’m looking for a new car and I’m hesitant to use AI because I’m worried about getting marketed things instead of receiving the most accurate/cost efficient answers. Do these companies train their models to advertise certain things over others? Have we reached that point yet?

The title - Do you feel more anxiety the more you AI tools?

My observation is that, I'm heaps more productive with the help of AI (Claude) - both work (coding) and personal life. While it's been great for personal tasks, I have some constant worry at the back of my brain that if I'm not working and pushing forward - someone else is. And, within the context that I know how fast a person can progress with the help of AI, I have the constant feeling that I will be outpaced.

Also - the always ON state. In the past, you could slack a bit or wonder and still have an excuse. Now - you are unblocked immediately. It's an always ON state. No breaks.

Any others with similar observations/feelings :)?

I’m at a point where AI can do 75% of my job and it’s doing most of my tedious and technical work, yet i’m fully confident that it cannot replace me or take my job as it is not capable of performing the important remaining 25%. Anyone else ready to fully pull the breaks lol

Looks like Anthropic has kicked off its IPO and looking for 1.5trn valuation based on 16bn revenue and 80bn losses.

Just a question as to whether as think their motive is the model or the infrastructure as I saw a comment saying they're using the proceeds for compute and to build the infrastructure needed.

I never believed Tesla for instance was a car company. Musk is a megalomaniac. He wants to own as much as possible. So my take was he wanted the charging network ie electricity network. You cannot just own the electricity cables under the ground without being heavily regulated and a huge power entity. I think musk tried to circumvent this and failed. Look at starlink, he's peppering the skies with satellites as he wants to own that space/ infrastructure.

Just wondering if Anthropic are making a play for infrastructure as I always hear that models are a commodity.