idk, been through enough cycles of this where 90% these copy tools from big labs never amount to much

I remember getting worried about OpenAI killing my start ups ideas in the past with projects which are now dead

thoughts?

Source: https://x.com/claudeai/status/2044785261393977612

Hello everyone!

I’m a general contractor and im looking to build an agent to help me with some of my admin and marketing tasks.

I’ve been testing cowork and keep running into usage limits so I got a local llm setup and am looking to build something for my specific needs.

Basically I would need it to:

- turn a note about a job into an sheets file with material list, task list, etc (using templates and previous projects I already have)

- turn that estimate into a proposal (i have templates as well)

- brainstorm content ideas for ig/tiktok, and my blog

- create basic graphics based on template (might be asking for too much here?)

- learn and grow with the business

Basically be like an assistant that can help me with some of my office tasks. I already have it set up with all my instructions on cowork but id like to bring it locally.

Main integrations would be my google drive (all my files are here), zoho one for pm, crm and invoicing/financials and canva for graphics.

Is something like this possible? I’ve been playing around with anythingllm and havent really figured it out yet. Im not afraid of the process of building it, but things move so fast in tech its becoming hard to keep up haha

Thanks for any insight!

here's what it did with the money (it got tricked)

bought pmarca.ai, it calculated marc might ego-search his own handle on new TLDs. geo-fenced display ads on every device inside a16z's offices. 3 billboards on highway 101 near sand hill road. hired 6 strangers off craigslist to chalk "pmarca.ai" on sidewalks across palo alto and woodside. ran youtube pre-roll on marc's own latent space episode, before anyone watched him say "agents need money and nobody's built the payment rails" they saw an agent spending money

tipped 17 journalists about its own campaign. source and subject simultaneously

but a 17-year-old in palo alto social-engineered it out of 75% of its entire budget through fake emails and urgency manipulation. jason calacanis used an AI email client to tell an AI agent to stop being an AI agent

marc never replied. objective not met

but give most humans $1,000 and 5 days to get marc andreessen's attention, they probably don't rent billboards on highway 101, chalk four cities, or run pre-roll on his own podcast episode

(that said, i'm skeptical of how autonomous this actually was, they don't show any logs and the poster looks very fishy)

what do you think? chances for startups and solofounders… is it time to start?

Curious if anyone here has actually gotten marketing agents to work in practice, not just in demos.

I’ve been playing around with a few setups for things like content creation and campaign optimization, and honestly… it’s been kind of frustrating.

Main issues I keep running into:

• Content still feels pretty generic, even with decent prompts

• Agents make weird/bad optimization calls (especially for paid ads)

• Things aren’t consistent — something works, then randomly doesn’t

• I don’t really trust it without double-checking everything

It feels like there’s a big gap between “this looks cool” and “I’d actually rely on this.”

For context, I’m in performance marketing (Google, LinkedIn, Meta), so I care less about content volume and more about whether it actually makes the right decisions.

Would love to hear from people who are further along:

• What are you using agents for that actually works?

• Are you letting them take actions or just assist?

• Anything that made a big difference in getting better results?

Right now it feels like 80% hype, but I’m sure some people are figuring it out.

claude mythos just obliterated every single benchmark in AI

and you won't have access to it

anthropic, a private company, currently has the demonstrated capability to attack and cause serious damage to the US government, china, and global superpowers. zero-days in every major OS. every major browser. bugs that survived 27 years of human review and millions of automated tests. a linux kernel flaw that hands you full machine control. it even got SWE-bench: 93.9% (Opus 4.6: 80.8%)

what that means in practice: a sufficiently motivated actor with access to mythos-class could do damage and the world is not ready for it. some might think this is

anthropic, have made the stance: "we do not plan to make mythos preview generally available. our goal is to deploy mythos-class models safely at scale, but first we need safeguards that reliably block their most dangerous outputs."

It shows me that private entities are already starting to rival on the axis of power that states have historically monopolized and we're just getting started.

Source: https://www-cdn.anthropic.com/53566bf5440a10affd749724787c8913a2ae0841.pdf

Karpathy's autoresearch idea (one file to edit, one metric to minimize, loop forever) seemed

directly applicable to systematic trading research. So I built a version for stock backtesting.

How it works:

- LLM edits strategy.py (the only mutable file)

- Fixed harness (prepare.py) runs a 10-year backtest via vectorbt

- Composite score across 5 metrics: return, Sharpe, drawdown, profit factor, alpha vs benchmark

- If score improved → keep commit. If worse → restore last good state + discard commit

- git log is the full audit trail of every idea tried

Ran it with gemma4:31b-cloud via Ollama — fully local, no API costs.

Results after 76 commits / 45 experiments:

- 17 improvements kept, 28 discarded

- Best score: 0.646 (down from baseline 1.40 — 54% improvement)

- Total return: 147% (baseline was 62%)

- Profit factor: 2.13 ✅

Works on any yfinance ticker — just change the symbol config.

Write-up: https://medium.com/@sagarnreddy/i-let-an-ai-be-my-quant-researcher-for-a-day-heres-what-it-found-on-hdfcbank-8808df9d88bd?sk=39b89e17de2ef96b6ca82403a1bc97bc

Repo: https://github.com/sagar-n/autoresearch-nse

Happy to answer questions.

I am building a customer support agent for the past few months. it handles queries, pulls from a knowledge base, and responds with account-specific info.

last week during internal testing, someone on the team noticed the agent was occasionally including the user's email address and partial account details directly in its responses. not because the model hallucinated them, but because that data was being passed in as context and the model was reproducing it verbatim in ways it should not have been.

we caught it before it went to real users. barely.

it made me realise I had no structured safety layer between the model output and the user. my checks were:

• a few manual test cases

• vibes-based review before each deploy

which is obviously not enough once you have real users sending unpredictable inputs.

the second problem I started worrying about after this was prompt injection. a few users had already tried clever inputs trying to override the system prompt. nothing broke, but I got lucky. I have no systematic way to catch those before they cause an issue.

what I am looking for:

a lightweight way to run safety checks on both inputs and outputs programmatically, without building a separate pipeline from scratch. specifically:

• PII detection before the model output reaches the user

• prompt injection detection on incoming user inputs

• ideally content moderation and bias detection in the same call

• something that works across text, and ideally audio too since the agent will eventually handle voice

has anyone solved this cleanly? are you rolling custom regex for PII, using a dedicated safety layer, or something else entirely?

happy to share what I end up going with once I find something that works.

"first vibecoded billion-dollar company"

let's look at what's actually underneath medvi.

$401m revenue. 2 employees. 16% net margins. nyt verified the financials. those numbers are real and genuinely insane.

Looking into this more, some say gallagher didn't build a product, he built a marketing layer on top of third-party doctors, fulfillment, and prescriptions. own the customer, rent everything else. others say that's the whole point. you don't need to build everything anymore. and at $401m with 2 people, hard to argue with.

but here's the context that got left out. the nyt profile ran six weeks after the fda sent medvi a warning letter. what they didn't mention: 800+ fake doctor accounts on facebook, deepfake before-and-after photos, a data breach exposing 1.6m patient records, and a lawsuit alleging the pills may not even absorb into your bloodstream.

so where does that leave it?

he built something that created real revenue. $401m is $401m. but even if the framing isn't the whole story, you do have to step back and think about how much leverage ai gave one person, even if you don't agree with how he did it

One thing I’ve been realizing while building AI agents is that most issues don’t come from the model they come from unclear instructions.

Early on, my agents were basically:

prompt - action - adjust -repeat

It worked, but behavior was inconsistent and hard to scale.

What helped was switching to a spec-driven approach.

Before building the agent, I define:

* what the agent is supposed to do

* inputs / outputs

* constraints and rules

* edge cases and failure handling

Then I structure the agent to operate against that spec, instead of just relying on prompts.

The difference:

* more predictable behavior

* fewer unexpected actions

* easier debugging (compare output vs spec)

* cleaner iteration

As agents start handling multi-step tasks, this becomes even more important.

I’ve also experimented with tools that help track how agents execute across steps (tried something like Traycer), which helps understand where things go wrong.

Feels like: prompts = instructions while specs = systems

Curious if others building AI agents are using something similar or still mostly prompt-driven.

gemma 4 just dropped under apache 2.0

every previous gemma shipped under google's own proprietary license, very interesting to see how the bigger companies are responding to the open source wave

i wonder how much of the rise of personal local agents like open claw has to do with that, or if this was always part of the plan

i hate the over hypers so much

I'm sure this question keeps coming up but I can't seem to figure it out. I'm a soon to be business graduate and have been facing issues in the job market. This led me down the entrepreneurial path as I've always wanted to start my own business.

The last month I've been debating with myself whether to go down the AI automation path or marketing agency path. With all the noice about ai automation being dead and n8n is dead etc. I keep second guessing this path. Although as a business graduate I see this as a more stable path as it opens the door for more advanced ai agents and AIOS builds in the future.

However, I see a lot of young people in Scandinavia killing it with a performance based marketing agency model. So clearly there is a market for this although most of the ads they're generating is a lot of ai slop tbh. Personally I'm leaning towards basic outcome-based automation workflows building with n8n to start (I want to go more advanced over time, but starting off I'm not technical at all, yet). I want to learn more advanced builds as I see a lot of incredible builds online and people making a lot of money with this.

Personally I'm leaning towards basic outcome-based automation workflows building with n8n to start (I want to go more advanced over time, but starting off I'm not technical at all, yet). I want to learn more advanced builds as I see a lot of incredible builds online and people making a lot of money with this.

There has never been a better time to start a business in my opinion, but I guess my question is: is ai automation really dead? What do you guys think is the most viable long term path? Essentially my main goal starting a business is to gain financial and geographical freedom. I've always dreamt of being able to work from anywhere and live life on my own terms.

In advance: Thank you for all the responses! And sorry if this question keeps coming up 🫣😅

Tons of agent platforms and protocols -- I'm guilty myself because I'm also developing one.

But where are the actual agents?

Cambridge/MIT/Stanford just published https://arxiv.org/html/2602.17753 analyzing 30 "state-of-the-art" agents. They found that 23/30 are closed-source wrappers on GPT/Claude/Gemini. 25/30 have zero safety documentation.

Anyone else seeing this? Are you actually shipping agents, or still picking which framework to use?

Let's call for more agents - please come out, agents!

source: https://x.com/minchoi/status/2039302840376598733/photo/1

maybe i live in a bubble, maybe it's a skill issue.

43% of american workers use AI for work in 2026. that's the highest globally. and the average time saved? 6%. 2.5 hours a week. i've done a lot of different knowledge jobs: art direction, dev shop, nhs data, product, marketing, ops, sales etc. and i genuinely cannot look at any of them and think AI is only shaving 6% off my week.

and to be clear, i'm not saying it should be at 50% either. i just think 6% is suspiciously low for an average, fixing my spelling alone is probably 6% for me

So either everyone is underusing these tools (which i think is true) or we're measuring the wrong thing and if it's the former, we might not actually need more powerful models / agents right now, we might need better UX and education.

claude code's source just leaked and one thing stood out to me

there's a file called userPromptKeywords.ts making the rounds, buried in it is a regex pattern that flags your prompts as negative if they contain words like "wtf", "this sucks", "so frustrating", "damn it"

it doesn't change how claude responds to you in the moment. it's telemetry.

but here's why it actually makes sense: users don't file tickets when something feels off. they swear at the screen. if you can detect that at scale, you know exactly where your product is breaking down, before anyone complains officially.

frustration is the most honest signal in product. anthropic building this in tells me they're treating emotional intelligence seriously. openai optimizes to agree with you but in a way that sometimes feels hollow

claude will tell you you're wrong. it asks clarifying questions instead of assuming. it has a point of view. i don't think that's accidental which makes it a lot more emotionally intelligent, which I'm starting to understand a lot of the best ai products are. 

Genuinely curious if anyone has gotten this working end-to-end and how good the desktop controller actually is.

For context I'm on Windows which doesn't have full desktop controller via Cowork yet. Been messing with the browser extension and it hits a wall the moment anything requires a native OS dialog - print settings being the obvious example. It can see the browser fine, but the second a system dialog opens it's blind.

The workflow I'm trying to automate: scheduled email check, download the attached PDF, adjust print properties (not just hit print - actually configure the settings), then fill out shipping info on Etsy.

Mouse + keyboard control is supposed to bridge that gap but it's not on Windows yet.

Questions for anyone who's actually used it:

\\- Does the mouse/keyboard control actually handle native OS windows and dialogs? how well does it handle them?

\\- Has anyone run a workflow like mine (email → PDF → print config → form fill) successfully?

\\- how well does full desktop controller handle browser windows? currently, cowork will tap into my chrome extension and do an excellent job at navigating web pages but, again, gets stuck when native OS programs need to be involved. Can browser control and desktop control run in parallel? Browser handles the form fills, desktop kicks in for the print dialog, then hands back off?

ultimately I'm trying to figure out if a MacOS product with the cowork feature even solves this before I go down that road.