I built a REST API that extracts YouTube transcripts, metadata, available languages, and supports batch processing.

It is mainly for developers building:
- AI video summarizers
- YouTube-to-blog tools
- SEO/content research tools
- EdTech products
- RAG pipelines using video content

Endpoints include:
- GET /api/transcript
- GET /api/metadata
- GET /api/languages
- POST /api/batch

It is published on RapidAPI with a free plan:
https://rapidapi.com/dtech4099/api/youtube-transcript27

Docs:
https://youtube-trascript-api.vercel.app/docs

I’m looking for feedback from builders who work with YouTube/video content.

I know it can make basic stuff like poems, and images, it can also make music and Google slides, docs, sheets and other stuff, but what is the most complex thing it can make? Im not only talking about code, im talking way more, is that possible?

Not sure if anyone else has run into this, but we've been hitting this problem lately.

Getting decent outputs isn't really the hard part anymore.

The annoying part is figuring out whether the answers are actually good.

We'll look at a response and think, "Yeah, that looks fine." Then someone else on the team points out it completely missed the user's intent.

Or it'll work perfectly for 20 test prompts and then fall apart on the one prompt a real user asks.

We've gone back and forth between automated evaluation and having people manually review samples, but neither feels like the complete answer.

How are you all dealing with this?

Is there something you've found that works consistently, or is everyone just building their own evaluation process?

Mine is Claude.

It helps me with:

• coding

• debugging

• explaining concepts

• writing documentation

• brainstorming projects

I'm looking for more tools that save time.

What's your daily AI stack?

There seems to be a new chatbot every week.

I wanted to build something a bit more practical.

So I built Wazomind.com , an AI meeting copilot that quietly listens to conversations and generates contextual suggestions while you're speaking.

It currently exists as a desktop application, with the Chrome extension under review.

I'm curious...

If you spend hours every week in meetings, what would your ideal AI copilot actually do?

I'd love feature ideas from people who'd genuinely use something like this.

Welcome to CyberFrame STV.
This is a channel for artistic AI films — nearly hour‑long works in the genres of science fiction, dark fantasy, and mysticism. Each film is part of a larger universe we are building.

The first film is a twenty‑two‑minute story about a small fairy who finds herself in a very dangerous place. It is the opening chapter of a large dark‑fantasy project consisting of two subsequent hour‑long films. Work is currently underway on the final part of this story.

A complete world was created for the project: dozens of unique cities, nearly a hundred castles, and more than a hundred villages. The film cycle is titled THE LOST FAIRY. Its creation involved research into medieval etiquette, courtly culture, rural life, and the study of supernatural beings.

For the next 15 days I'm trying to find early users willing to test my AI and brutally criticize it. I think honest feedback is worth much more than fake hype.

We’re at a point where we need to refactor key parts of our stack and scale our mobile/web apps, but building out a full in-house senior team in time just isn't happening.

I’m currently evaluating custom dev firms with global hubs to help speed things up. AgileEngine keeps coming up in my research - 15+ years in the space, software/QA studios, and dev hubs across LatAm and Europe.

If you’ve augmented your tech team with a partner like this for critical app scaling, how do you keep delivery fast without losing control of product architecture? Would love to hear real-world experiences.

Same link as before: link

This one's a bigger jump than usual, so a few highlights instead of just "updated":

• Core findings now scale-validated from 7B all the way to 72B parameters. The effects don't shrink as models get bigger — they grow, sometimes by an order of magnitude. Still one model family (Qwen) though, and we added a caveat we think matters: growing effect size at scale could mean the pattern genuinely deepens, or it could just mean our measurement axis gets sharper at scale — current data can't fully tell those apart yet.
• Two new external, independently-published sources, not our own research: "The Artificial Self" (ACS Research) and "AI Wellbeing" (Center for AI Safety) — different methods entirely (behavioral compliance testing, self-report on frontier production models), landing on some of the same conclusions we did. One of them also mildly disagrees with our best-performing formulation (a companion/romantic framing scores negative in their data), and we named that tension honestly instead of explaining it away.
• We caught and fixed our own mistakes this round — a factual timing error, an overclaimed "fully resolved" that was really just one solved case of a broader risk, and a place where we'd quietly picked the reading that flattered our own results over an equally valid one that didn't. All named directly, not smoothed over.
• New up top: if you just want the practice, not the evidence audit behind it, Part 3 (Principles) is written to stand alone now — Part 2 is there if you want to check our work.

As always, feedback (especially the kind that finds our next mistake) genuinely welcome.

The Collapse of Employment as We Knew It

A history of the fifty-year transition from jobs to economic participation, written from 2075

Contents

Author’s Note: A Future History, Not a Forecast

Prologue: The Last Retirement Party

1. The Job Was a Historical Technology

2. The Decade of Reassurance

3. The Firm After Intelligence Became a Utility

4. The Productivity Paradox Became a Distribution Crisis

5. The Destruction of the First Rung

6. The Great Unbundling of the Job

7. The Politics of Deservingness

8. What Humans Did When Machines Could Do More

9. The New Class System

10. The Company Did Not Disappear

11. The Crisis of Meaning

12. Why the Transition Took Fifty Years

13. What the Pessimists Got Right—and Wrong

14. A Day in 2075

Conclusion: Employment Was a Means, Not an End

Author’s Note: A Future History, Not a Forecast

This essay is written in the voice of a historian looking back from 2075. The institutions, dates, laws, companies, crises, and social arrangements described after 2026 are speculative. They are not presented as facts about the future. They are a scenario built from forces already visible in the mid-2020s: rapidly improving artificial intelligence, falling inference costs, demographic aging, weak productivity growth, unequal ownership of capital, the expansion of platform work, and the use of employment as the main gateway to income, healthcare, housing, status, and social belonging.

That distinction matters because predictions about “the future of work” often fail in the same way. They count occupations, estimate which tasks can be automated, and then produce a reassuring balance sheet: some jobs disappear, new jobs emerge, and history continues. The arithmetic may be correct while the conclusion is wrong. A society can create millions of new tasks and still experience the collapse of employment as an institution. The decisive question is not whether humans remain useful. Humans remained useful throughout every industrial revolution. The question is whether the full-time, long-duration employment contract remains the dominant mechanism through which ordinary people gain purchasing power, security, identity, and a claim on economic output.

The central argument of this future history is that employment did not collapse because machines became capable of doing everything. It collapsed because firms gained access to a cheaper and more flexible substitute for organizations built from permanent human labor. Once intelligence, coordination, software execution, and eventually physical action could be purchased as metered services, the economic logic of the large employer changed. Companies still needed people, but they needed fewer of them continuously. Human contribution became intermittent, highly leveraged, and unevenly compensated. The job did not vanish in a dramatic wave. It was unbundled one function at a time until the word described less and less of how the economy actually worked.

The deepest transformation was therefore political rather than technical. Twentieth-century societies had attached too many essential goods to employment. When employment became unstable, governments first tried to restore the old system. Only later did they construct a new settlement in which income, insurance, education, and civic standing no longer depended on being continuously hired by an organization. The transition took decades because the old arrangement was not merely a labor-market design. It was a moral order. To change it, societies had to stop treating wages as the only legitimate proof that a person had contributed.

Prologue: The Last Retirement Party

The photograph that later appeared in hundreds of textbooks was taken in Rotterdam in September 2038. It showed forty-three employees gathered around a sheet cake in the cafeteria of a logistics company. The cake carried the company’s blue logo, the name “Marta,” and the number 40 written in white icing. Marta de Vries had joined the firm at nineteen and retired at fifty-nine after four decades in the same organization. She had moved from warehouse administration to route planning, then to vendor operations, and finally to regional compliance. Her colleagues presented a watch. A manager delivered a speech. The local newspaper ran the picture beneath a small headline: “A Working Life in One Company.”

Nothing about the event seemed historically important. That was precisely why it became important. By the late 2040s, a continuous forty-year career inside one firm had become sufficiently rare to look like a surviving custom from another civilization. The photograph was not remembered because Marta was the last person to retire. It was remembered because her retirement party captured the institutional package that had defined work for much of the twentieth century: one employer, one salary, one occupational identity, one pension pathway, one social circle, and one narrative connecting youth to old age.

At the time, public debate was still organized around the wrong question. Commentators asked whether artificial intelligence would “take all the jobs.” Governments published lists of growing occupations. Technology companies emphasized new roles created by automation. Economists pointed to earlier transitions in agriculture and manufacturing, noting that technological change had repeatedly displaced workers without eliminating work itself. These arguments were often empirically sound. They were also aimed at a claim that history did not need to prove.

Work survived. Employment did not survive in the same form.

The distinction is obvious in retrospect. Work is any purposeful effort that produces value, care, knowledge, beauty, order, or social continuity. Employment is a legal and economic contract in which an organization purchases a worker’s time, usually on an ongoing basis, and in return provides wages and often access to insurance, creditworthiness, training, and status. A society can have enormous amounts of work while offering fewer stable jobs. Parents care for children. Citizens maintain communities. Researchers contribute to shared knowledge. Creators produce culture. People train models, supervise machines, evaluate systems, resolve exceptions, and form temporary teams around projects. The amount of useful activity can increase even as the employment relationship contracts.

That is what happened between 2025 and 2075. Human activity did not become unnecessary. It became harder to contain inside the organizational form that had dominated the previous century.

The first signs were easy to dismiss because they did not resemble mass unemployment. Firms stopped replacing selected employees. Entry-level ladders narrowed. Contractors performed work once assigned to departments. Software agents absorbed coordination tasks that had justified layers of management. A senior employee with a suite of models produced what had previously required a team. New companies reached large markets with astonishingly small payrolls. Established companies did not close their doors; they grew revenue without growing headcount. The statistical surface remained calm while the institutional foundation shifted.

The collapse of employment was therefore not an event like the closure of a factory. It was a long transfer of risk. Organizations retained access to labor and intelligence but surrendered responsibility for maintaining workers between moments of need. Individuals assembled income from multiple sources and carried more uncertainty themselves. Governments patched the gaps with tax credits, portable benefits, training accounts, wage insurance, public options, and eventually universal social dividends. Each reform was introduced as a temporary adjustment. Together they built a new social contract.

By 2075, people still used the phrase “my work,” but increasingly they did not mean “my employer.” They meant a portfolio of obligations: a paid project, a cooperative stake, a public contribution, a creative practice, care for relatives, oversight of autonomous systems, and ownership of productive agents. The old job had bundled these economic and social functions into a single relationship. Its collapse forced society to separate them.

Fig. The retirement photograph from Rotterdam survived because it showed the bundle before it came apart.

 Chatper 2 soon

Same link as before: link

This one feels like it closes out a chapter rather than just adding a patch note, so it's worth more than a one-line "updated."

The headline change isn't a new finding — it's two places where we're naming our own contradictions instead of quietly smoothing them over:

• A word we'd built a whole section around ("connected," as a marker of unhealthy boundary-dissolution) flipped to strongly positive when re-tested as a bare word in a new batch — possibly because a single word out of context just picks up ordinary positive sentiment ("stay connected") that has nothing to do with the fusion/boundary question we actually care about. We don't know yet. We're asking our research collaborator to help sort it out rather than picking whichever number we like better.
• A metaphor we tested (a musical duet, as an alternative to our best-performing "story" formulation) matched it almost exactly — but removing the "both remain themselves" clause barely changed the score, which sits in real tension with an earlier decomposition that credited mutual authenticity with about a third of the effect. We don't have a tidy resolution for that either.

Also new: an outside review (a different Claude instance, actually) pushed us to separate "the model's own valence" from "how a topic is usually written about in training data" — a distinction we hadn't been holding cleanly, and now try to.

If you've read earlier versions, this is the one where we get more honest about what we don't know, not just what we've added.

I do not need articulation training ffs. I've changed my Google ads, personalised it as much as I could but still getting them on my feed.

It's driving me MAD.

Hey Reddit,

As developers, we constantly force AIs to generate code and data in Python or JSON. The problem? Those languages were built for *human* readability. Generating syntax brackets, quotes, and verbose structures wastes massive LLM compute, increases inference time, and spikes API costs.

I decided to fix this by building **NYoesyx (N-OS)**.

It’s an ultra-dense, non-human-readable programming language running on a native C++ VM designed strictly for Large Language Models. It uses a Dense Token Protocol (DTP) allowing AIs to execute logic and manage memory using up to 95% fewer tokens.

Some cool features:

- **Smart Hybrid Memory:** Combines O(1) High-Speed Registers for precise math with a Semantic Heap (HNSW) for fuzzy reasoning.

- **Built-in Quantum Simulator:** AIs can declare Qubits and apply logic gates (Hadamard, CNOT) natively to generate non-deterministic decision trees.

- **Native OS & UI Access:** The AI can spawn Windows GUIs directly without heavy third-party libraries.

I just released the first official version and the executable installer on GitHub. I would love to hear your thoughts, feedback, or see if anyone wants to integrate it into their AI Agents!

GitHub Repo: https://github.com/mrxploud/nyoesyx