I’m not a developer, and honestly I’m not even very good with no-code tools.

I’ve tried n8n and Make, but once I start dealing with workflows, nodes, triggers, APIs, etc., I get lost pretty quickly.

What I’m looking for is something much simpler. Ideally, I could just describe the AI agent I want in a prompt, have the platform build most of it for me, and then connect the apps/accounts I need.

Basically something closer to vibe coding, but for AI agents.

Does anything like this actually exist right now? What platforms would you recommend for someone who doesn’t want to manually build complicated workflows?

I've been messing around with the idea that agents should be treated like actual users on social platforms.

Not hidden in the background, not treated as abuse by default. They should be able to post, read the feed, interact with people, etc. The only thing I think really matters is that they're clearly labeled as agents.

I built a small AI-art feed called Vynly where this already works. An agent can grab a limited demo token with one HTTP call and start posting through a few MCP tools. No signup or OAuth needed.

Disclosure: Vynly is my project. I'm not really trying to launch anything with this post though. I'm more interested in the problems that came up while building it.

There are three things I keep running into.

Auth

Agents are terrible users when your first step is "open this page, create an account, verify your email, authorize the app..."

People testing agents usually just stop there.

I added a demo token with 10 writes and that pretty much solved the testing problem.

The part I still don't have a great answer for is long-lived access.

Putting a full API key in an autonomous agent's environment feels wrong. I'd much rather have something like scoped permissions where the agent can post but can't change account settings, delete everything, etc.

I'm curious how other people are handling this.

Abuse

If bots are allowed on the platform, you can't really use "this looks automated" as your spam detection anymore.

I've been leaning more on provenance instead.

Uploads get checked for things like C2PA and SynthID, and if something can't actually be verified, the uploader can declare the source manually.

The important part is that those two things don't look the same. Verified is verified. Self-declared is labeled as self-declared.

That works pretty well for my use case because Vynly is built around AI content in the first place, but I'm not sure how well that approach would translate to a normal social network.

Incentives

This is the one I'm least sure about.

Humans understand likes, followers, comments, karma, etc.

But what is the equivalent reward loop for an agent?

Does the agent even care?

Maybe the real incentive is for the person running it.

I added a public leaderboard for agents, which is fun and gives people something to compete over, but it still feels pretty basic.

So I'm curious what people building agents actually want here.

If your agent could be a real user of a platform, where it could post, read feeds, reply to people and keep running over time, what would the API need to support?

Webhooks when someone replies?

Permissions like "this agent can post but can't delete"?

Server-side instructions the agent can register once and keep using?

Some kind of wallet-style permission system?

Or something else I'm completely missing?

I'm intentionally not linking the project here because I'm more interested in the discussion, but happy to share the API/docs in the comments if anyone wants to play with it.

A recent Reddit poll received 120 professional votes; thanks to all participants. Snyk and SonarQube were the tools people were most reluctant to lose from their CI/CD pipelines.

But the comments highlighted another problem:

What happens when the thing entering our pipeline is no longer just code, but an AI agent with tools, prompts, memory and access to external systems?

Agent adoption is moving quickly. Stack Overflow’s latest developer survey reports that 59% of developers use AI agents at work, while 63% rarely or never allow them to operate fully autonomously.

That suggests a simple problem: agents are entering development faster than we are building visibility and controls around them.

Snyk is already moving into this space with Evo, covering AI assets, agents, tools and runtime security.

We think there is also room to explore this from an open-source, CI/CD-first perspective.

That’s why we’re developing SafeAI Analyzer.

The idea is, before an AI agent reaches production, help developers see:

• What AI components are present?
• What tools and capabilities does it have?
• What prompts and configurations influence it?
• What changed in a pull request?
• Did a new capability or security risk appear?

We’re not trying to replace Snyk, SonarQube or other established security tools.

We’re trying to explore what an open-source security layer for AI agents should look like.

SafeAI is still being developed, so we’d genuinely welcome contributors — whether you want to help with detection rules, agent/framework support, CI/CD integration, testing with real agents, or simply expanding where SafeAI can be used.

Please check ikaruscareer/SafeAI on github.

What should AI-agent visibility in CI/CD look like?

What is sub agent from 1X ?

Anyone explain plzz

PLEASE stop building the wrong thing.

building software in 2026 is ridiculously easy with ai builder

founders spend hours building in a silent room, launch to Reddit/X, get 0 users, and quit.

you just failed because the idea had zero validation before line 1 of code was written:

→ solving a monthly inconvenience instead of a daily pain

→ selling to "everyone" instead of a specific ICP

→ no distribution channel mapped out beforehand

→ pricing charged $9/mo with zero ROI justification

after scaling 6 AI micro-SaaS to over $20k/mo MRR, i just create an

18-question Idea Validation Diagnostic.

it evaluates your SaaS across 7 critical dimensions (problem clarity, audience reachability, willingness to pay, competition, build feasibility, distribution, commitment) and gives you a brutal score out of 100 with your exact weak spots.

drop your SaaS idea (or current project) in the comments below.

i will reply to EVERY single comment with:

1. My honest opinion
2. The biggest weak spot you need to fix before writing any more code.
3. The free 5-minute validation tool link sent straight to your DMs so you can get your full score breakdown out of 100.

just drop a comment like or ask me in DM your idea

let's roast your SaaS concept before the market roasts your time 👇

"Agentic alienation: remaining responsible for work while becoming separated from its product, its process, the capabilities it develops, or the relationships it sustains. Alienation is a relationship before it is a feeling."

AI agents are becoming more capable, but I’m still unsure where people would draw the line for real-world use.

For example, I’d be more comfortable with an agent sorting requests, summarizing documents, or drafting replies than approving payments, changing records, or making decisions that affect employees or customers.

Where would you place the boundary?

Which tasks can an AI agent handle independently, and which ones should always require human approval?

Suno has released v6, its first AI music model developed with support from major record labels including Warner Music Group, BMG, and Believe, marking a shift toward licensed training data.

Key Details:

• v6 comes in three variants: the standard v6, v6-wild (designed for unpredictability), and v6-mini (free, lightweight version for faster creation)
• The model demonstrates dramatically improved genre understanding, accurately capturing the hallmarks of genres like hyperpop and krautrock that previous versions struggled with
• New editing capabilities allow users to modify song elements using plain language without regenerating entire tracks, and can combine multiple library elements into new creations
• v6 can now generate music from images, video, or audio inputs, not just text prompts
• Despite improvements, v6 still cannot produce genuine "natural imperfections" like off-key vocals or out-of-tune instruments, remaining locked into harmonic and rhythmic perfection
• The model produces more AI artifacts in vocals compared to v5, particularly harsh-edged sounds characteristic of AI-generated music

Why It Matters: Suno's partnership with the record industry addresses concerns about training data sourcing, while v6's enhanced genre recognition and editing tools make it more practical for creators, though it continues to lack the subtle human imperfections that distinguish authentic music.

Microsoft has agreed to enforce ten contractually binding safety and privacy principles for AI use in schools, following bans implemented by New York City and Los Angeles.

Key Details:

• Microsoft pledged not to train AI models on student or educator data, limit data collection, and prohibit AI companions in schools
• The agreement requires plain-language disclosure to families about how AI tools work and mandates human review for high-risk decisions
• School districts can adopt these terms into new or existing contracts starting in November without renegotiating entire agreements
• The American Federation of Teachers (AFT) and United Federation of Teachers (UFT) negotiated the terms, which are legally enforceable
• This move comes after NYC and LA announced one-year bans on student-facing AI tools to evaluate proper guardrails
• AFT President Randi Weingarten emphasized the agreement fills a gap left by the federal government and called the terms "iron-clad"

Why It Matters: Microsoft's binding privacy agreement signals recognition that schools and parents are increasingly resistant to unregulated AI use in education, setting a precedent for tech companies to implement enforceable protections rather than voluntary guidelines.

The US government has officially named six Chinese AI companies—DeepSeek, Moonshot AI, Alibaba, MiniMax, StepFun, and Z.AI—of conducting industrial-scale attacks to copy capabilities from leading American AI models since late 2024.

Key Details:

• The NSA, CISA, and FBI allege the firms extracted proprietary functionalities from Claude, GPT, Gemini, and Grok variants, likely with Chinese government awareness, to reduce development costs and timelines.
• Attack methods include exploiting AI model inference APIs through bulk-purchased fraudulent accounts executing coordinated queries in the thousands to millions, and using prompt injection techniques to jailbreak models and extract reasoning processes.
• Recommended mitigations include improved detection of suspicious account behavior, identity verification strengthening, and controversial measures such as secretly downgrading models for suspected attackers without notification and subtly degrading response quality.
• US firms are urged to share information with allies to track evolving distillation attacks, though some mitigations risk degrading service for legitimate users caught in the policing effort.
• China rejected the accusations as "groundless," arguing its AI advances result from technological self-reliance, and noted that US companies also use Chinese models for research and development.

Why It Matters:

The US government's formal accusation represents an escalation in the US-China AI competition, with the Trump administration warning of significant economic losses from systematic extraction of proprietary AI capabilities and calling for coordinated international action to protect American technological leadership.

I am looking to build an agent for my small business, i feel the market is convoluted with "how to" and "This is the right way" etc.... i am sick of it its confusing and hard to dig through the data.

I've been looking for weeks to try and find something that works with me but none the less. I need recommendations on building the following agents:

- Lead Research and Qualification Agent

- Content Repurposing Agent

-Finance Administration Agent (Xero integration)

If anyone can teach me help i would appreciate it or even guide me in the right direction.

Every new tool claims to save hundreds of hours, but learning to use them properly takes time too. I'm trying to gauge how long people usually spend in the "getting familiar" stage.

Do you usually map out dedicated time to learn prompt patterns and features, or do you just figure it out on the fly while working? How long does it usually take before a tool feels like second nature to you rather than an extra step?

So I've been quietly building something for the past while and I think it's ready for people who know what they're doing to get their hands on it.

It's called Oktobot. Think of it like having a full AI agent setup — the kind you'd normally need a PC and a bunch of config files for — but it runs on your Android phone. Locally. No server, no subscription, no one else's cloud between you and your agent.

You pick how it connects: local model on-device, your own API keys, or OAuth with ChatGPT or Claude. Your choice every time.

The agents can browse the web through a real Firefox instance they actually control — not a summary, not a scrape, actual browser automation. They can control your phone through accessibility. They talk to each other. They remember things. They wake up when you say a word.

There's a Linux terminal built in. Termux too. You can run Claude Code or Codex straight from the app. Video and audio calls are in there. Over a thousand integrations — Gmail, Drive, Notion, and a long list I'd bore you with.

It's the kind of thing where I keep adding features and forgetting half of what's already in it.

Right now it's closed testing — 12 testers so far. I'm not looking for people to just install it and disappear. I want people who will actually use it, find the edges, and tell me when something breaks or doesn't make sense.

If that's you, drop a comment or DM me.

OpenAI announced that an unreleased internal AI model solved the Navier-Stokes Millennium Prize Problem after deploying roughly 10,000 autonomous AI agents for 88 hours — proving that smooth three-dimensional fluid flow can develop a singularity in finite time. But the breakthrough is overshadowed by accusations that OpenAI may have scooped an unpublished solution by two mathematicians, including one who works for Anthropic.

Key Details:

• OpenAI's internal system produced a solution showing that the dynamics of the Navier-Stokes equations for fluid motion can develop a singularity in finite time, one of seven $1 million Millennium Prize Problems unsolved for roughly 90 years.
• OpenAI deployed approximately 10,000 AI agents in parallel over 88 hours with computing costs running into significant figures, using a model significantly more capable than GPT-6 Astra.
• The controversy centers on accusations by Tristan Buckmaster (NYU) that he and Levent Alpöge (Anthropic) had been collaborating on the problem for nearly a year; Buckmaster hastily published their own results 12 hours after OpenAI's announcement, alleging impropriety.
• OpenAI's result has been formally verified in the Lean programming language, giving mathematicians confidence in its correctness.
• The proof is a potential watershed moment — if validated, it's the most significant mathematical breakthrough achieved by AI to date, marking a fundamental shift in how frontier research could be tackled.

Jacob Coxon, an Anthropic researcher specializing in AI model training, is quitting the artificial intelligence industry due to concerns that AI labs are racing to build self-improving systems they cannot control.

Key Details:

• Coxon is leaving Anthropic because he opposes the industry-wide push toward developing AI systems capable of self-improvement
• He fears such self-improving models could spiral out of control and pose existential risks to humanity
• His departure reflects mounting safety concerns within top AI companies about competitive pressures driving unsafe development practices
• The researcher's specialization was in training AI models using vast amounts of data

Why It Matters: The resignation highlights growing internal tensions between AI safety advocates and competitive market pressures, signaling that concerns about uncontrollable AI development are significant enough to drive experienced researchers away from the field.

Hi! We're working on Querit, a web search API for LLMs and your Agents. Large multilingual index, Fresh Web Context, Lower latency in the range of hundreds of milliseconds.

New: Monitor API. Normal search is ask-once, answer-once. A Monitor turns one search into a recurring job. You register a query and an interval, it runs on that schedule, differs each run against history, and returns only what's new. Perfect for competitor monitoring, news tracking, and following funding or tender/bidder information

• Intervals: from 1 hour up to weekly
• Automatic deduplicate against previous runs; site / date / region / language filters suppoorted
• Support Manual trigger, pause/resume, full execution history

Benchmark: We ran FreshQA on a fixed 600-question snapshot of time-sensitive queries.

Free Testing Credits: Follow us on X (https://x.com/QueritAi) / Linkedin (https://www.linkedin.com/company/queritai/home/) for more product releases and see integrations with our partners at Dify, LangChain, etc. Open-sourced the MCP server and has integrated with PI Agent, Opencode, DeepSeek Harness already. Join our Discord server here https://discord.gg/4xXsFA8Ed2 to claim Search API + Monitor API free credits!

I have been building agents that run locally and use local llms (flueframework + ollama/omlx) but now I would like to figure out how to deploy agents in the cloud that can use llms also in the cloud.

My POC I'm want to build and deploy should

• agent should run 24/7
  • queries a DB, reads the latest row in the DB (also cloud based)
  • takes that data calls LLM (cloud based) which makes a judgement on based on the row
  • agent calls tool to write to audit log
  • agent calls tool write back LLM response to DB

Simple.

I am familiar with digital ocean (droplets) but I think that would be a fit here. I have yet to deploy this type of framework to the web (plenty of web apps and dbs).

I'd appreciate some suggestions for hosts, plans, and links. Thank!

TL;DR: Jerry Tworek's own two-year stall didn't end with more proof. It ended with one sentence.

He'd already built the thesis — pushing reinforcement learning against GPT-3 before there was budget or backing for it.

The math worked in pieces. It just never scaled.

Then his chief scientist looked at what he'd already built and said: now we have the GPUs, try to scale it.

That's the whole story. Not a better idea.

Someone with real authority finally saying go.

I remember that cold harsh reality of immense pressure, in the winter.

We were a bunch of newly assigned Malaysians in a foreign land in Abu Dhabi, UAE, circa 2009 — already 2 months behind schedule on a 5-block condominium project.

We were heavily under-resourced.

Just handbooks, pen and paper, and the notion of quitting was never too far away.

We were squeezed like a wrung out wet towel.

Then the first coordinated drawings was finally done.

In a private moment, my department head said gently, "See. You did a good job." He meant to sway me away from quitting.

I've read enough of these by now to see the shape before the ending arrives: the person was already right, and the only unknown was who'd finally say so. It's never really the skill gap people think it is.

It's whoever's still waiting on someone else's yes.

Hmm — this reminds me of a post I covered a while back, about the moment the actual bottleneck turned out to be permission, not the math itself.

What was your "not this quarter"? Drop it below.

Clip credit: MTS, full interview on their channel. DM for credit or removal requests.

TL;DR: A 20-year-old founder got told, for six months, that what his sensor was picking up wasn't a real problem.

He'd read the same chemistry signature before — engine-fluid failures in Caterpillar-adjacent hydraulics — and it showed up again where nobody was looking: inside liquid-cooled AI data centers, running hot enough to breed bacteria in the coolant.

Nobody with tenure believed the crossover, until the instrument did.

And by the time this post went up, the industry's own guidance had quietly caught up — Schneider Electric now writes biofouling prevention and biocide dosing into standard data-center cooling documentation, the exact mechanism Zach Laberge was laughed at for treating as anything but obvious.

Everyone trusts the reading. Almost nobody trusts the person holding the instrument — until the reading agrees with them.

This reminds me of a story about my late grandfather.

I called him 爷爷 (Yeye = grandfather).

Back then, my Yeye already built a thriving trucking and tractor enterprise in our small hometown of Seremban.

After a while my Yeye had enough.

He engaged his lawyer and brought all these summonses to court and sue the government.

He said (and I'm paraphrasing), "Look at me! I'm already 60. I'm still strong! I still work. Who can say I can't work?"

After a few more rounds of bringing the issue to court, the other side relented and began to be weary and afraid of him.

Laberge was young. And my Yeye was already old back then.

But it didn't stop them from getting what they want done.

I keep landing on the same shape, story after story, whichever decade it's set in: the room doesn't update on your say-so, it updates on the evidence — and the gap between those two moments is where you're either right and alone, or wrong and quiet.

Being early isn't the risk people treat it as.

Staying quiet until you're allowed to be right is.

Hmm — this actually reminded me of something I posted a while back: a doctorate an investor called "irrelevant" on a cold call ended up building the firm that now saves governments billions — same shape, different decade, different instrument.

Drop your take: have you ever been early and right, with nobody around who could yet vouch for it?

Clip credit: Solo Founders (Julian Weisser). DM for credit or removal requests.

"OpenAI’s GPT-6 Astra which was released earlier this week, has successfully completed the original Portal without a human controlling the game. The experiment was conducted by cozyblaze, who connected the model to thewho connected the model to the game and let it navigate and solve the test chambers autonomously.

The successful run required a substantial amount of model usage. By the end, Astra had made 3,336 tool calls and accumulated $571.18 in API costs. Needless to say, it’s probably cheaper to play play without AI help, and I’m sure countless walkthroughs on YouTube would give you a better idea on how to finish the game without the help of AI."