3 ms·

the issue most of you seem to not realize is that when you put these models in a loop, you are able to do more and more insane and cool things. have you guys a

the issue most of you seem to not realize is that when you put these models in a loop, you are able to do more and more insane and cool things. have you guys actually designed, built, and deployed agentic workflows? it is actually quite hard, requires tons of time spent on evals and testing to ensure accuracy, but when it starts to work it is mind blowing. there is no going back. listening to people yap about AI when they have only surface level or one dimensional exposure to LLMs and "AI", but have not actually put innovations to work IN PRACTICE.. is a waste of time

lolakutty 10d ago
> when you put these models in a loop, you are able to do more and more insane and cool things... Please share some of these insane things that you speak of..
- moomoo11 10d ago
  i mean have you used any coding agents? if you’re getting slop code in 2026, that’s a smell and skill issue. fwiw i was pretty bearish on AI until i spent a month a couple months ago going deep into agentic workflows. use your imagination to solve problems people face and pay $$$ for today that is error prone and hard. i’ve got agentic workflows for the particular industry im building for, one of which that replaces the need to hire $500+/hr services. in this particular workflow (don’t want to reveal too much, sorry this is my competitive advantage but you can figure it out for your own workflows) a $4/1M model ingests a file that is currently used in a extremely complicated program that few people understand how to use. it parses the data, loads it into a database, and then spawns a bunch of other agents that check the data against work in flight. there’s checks for bad data. in that case, more agents are spawned that reach out to the involved people or parties for clarification. if it cannot figure something out it reaches out to the right contacts for more information. while this is happening, more agents begin doing work that involves continuous reconciliation against 100s or 1000s or more things in flight. as files are uploaded, or updates from people come in, agents do work to ensure things remain on track. people are able to work across languages and cultures, and my agents ensure that while people can make mistakes, it will catch them in real time and ensure continuously monitor the situation. it’s pretty nuts how much inefficiency agents today can solve. it takes patience to run tests and tweak shit until it works. *** the really cool thing is that more capable agents can continuously monitor how things are going and improve the workflow itself… so all i need to do is maintain the actual tests. **** i loved writing tests back in the day to ensure i built good software. today we write tests to ensure the business can run.
  - StevePrefontain 10d ago
    Very hard to tell if this is satire. It reads like one of those old r/programmerhumor posts where people competed to come up with the worst designs possible for a volume control. I could not think of a worse technology to use for an ETL pipeline than throwing LLMs at it and asking it to vibe out the correctness of the data every time it runs.
    - moomoo11 10d ago
      lol. wish it was. fwiw i was pretty much bearish on AI for years because I did go deep into this shit since i got access to GPT 3. but since astra i have been quite bullish. 98% of people think AI means what gemini tells them when they do a google search. of that 2% who go beyond... maybe 20% of those are using AI to code. most SWE still think "using AI" to code means the copilot pane they open on the side in VSC. they get sloppy code output and think "ai sucks!" so of that 20%, maybe 5% have actually explored what an "agentic" workflow even means. they might use skills, set up their code base so AI almost NEVER makes a mistake. maybe 1-5% of that 5% actually went deeper, and those are the people who built tools like Cursor or Harvey or whatever other "agentic" companies. you are still thinking about the old world where you obsess and define data models and bike shed over data integrity, all the while you have a "temporary" table with 3 attributes that gets 2 million queries per second that's now holding up a bunch of other shit that's also glued together. > I could not think of a worse technology to use for an ETL pipeline than throwing LLMs at it and asking it to vibe out the correctness of the data every time it runs. like i said, if you are still having quality issues in 2026, that's a skill gap. also, agents now continually improve the process. for a business, the only thing that matters is transaction log. for 99% of businesses, swe are a cost. again this is like baby steps on the journey. we are still only 3 years into this technology being opened up to masses. i'm sure people thought computers were dumb, or that cars are stupid because the first cars were moving slow af. "we have horses, why do we need to build out roads to get anywhere" but you're free to feel smart doing 20/20 hindsight on things 100+ years from the future
      - lolakutty 10d ago

lolakutty 10d ago

> when you put these models in a loop, you are able to do more and more insane and cool things... Please share some of these insane things that you speak of..

moomoo11 10d ago
i mean have you used any coding agents? if you’re getting slop code in 2026, that’s a smell and skill issue. fwiw i was pretty bearish on AI until i spent a month a couple months ago going deep into agentic workflows. use your imagination to solve problems people face and pay $$$ for today that is error prone and hard. i’ve got agentic workflows for the particular industry im building for, one of which that replaces the need to hire $500+/hr services. in this particular workflow (don’t want to reveal too much, sorry this is my competitive advantage but you can figure it out for your own workflows) a $4/1M model ingests a file that is currently used in a extremely complicated program that few people understand how to use. it parses the data, loads it into a database, and then spawns a bunch of other agents that check the data against work in flight. there’s checks for bad data. in that case, more agents are spawned that reach out to the involved people or parties for clarification. if it cannot figure something out it reaches out to the right contacts for more information. while this is happening, more agents begin doing work that involves continuous reconciliation against 100s or 1000s or more things in flight. as files are uploaded, or updates from people come in, agents do work to ensure things remain on track. people are able to work across languages and cultures, and my agents ensure that while people can make mistakes, it will catch them in real time and ensure continuously monitor the situation. it’s pretty nuts how much inefficiency agents today can solve. it takes patience to run tests and tweak shit until it works. *** the really cool thing is that more capable agents can continuously monitor how things are going and improve the workflow itself… so all i need to do is maintain the actual tests. **** i loved writing tests back in the day to ensure i built good software. today we write tests to ensure the business can run.
- StevePrefontain 10d ago
  Very hard to tell if this is satire. It reads like one of those old r/programmerhumor posts where people competed to come up with the worst designs possible for a volume control. I could not think of a worse technology to use for an ETL pipeline than throwing LLMs at it and asking it to vibe out the correctness of the data every time it runs.
  - moomoo11 10d ago
    lol. wish it was. fwiw i was pretty much bearish on AI for years because I did go deep into this shit since i got access to GPT 3. but since astra i have been quite bullish. 98% of people think AI means what gemini tells them when they do a google search. of that 2% who go beyond... maybe 20% of those are using AI to code. most SWE still think "using AI" to code means the copilot pane they open on the side in VSC. they get sloppy code output and think "ai sucks!" so of that 20%, maybe 5% have actually explored what an "agentic" workflow even means. they might use skills, set up their code base so AI almost NEVER makes a mistake. maybe 1-5% of that 5% actually went deeper, and those are the people who built tools like Cursor or Harvey or whatever other "agentic" companies. you are still thinking about the old world where you obsess and define data models and bike shed over data integrity, all the while you have a "temporary" table with 3 attributes that gets 2 million queries per second that's now holding up a bunch of other shit that's also glued together. > I could not think of a worse technology to use for an ETL pipeline than throwing LLMs at it and asking it to vibe out the correctness of the data every time it runs. like i said, if you are still having quality issues in 2026, that's a skill gap. also, agents now continually improve the process. for a business, the only thing that matters is transaction log. for 99% of businesses, swe are a cost. again this is like baby steps on the journey. we are still only 3 years into this technology being opened up to masses. i'm sure people thought computers were dumb, or that cars are stupid because the first cars were moving slow af. "we have horses, why do we need to build out roads to get anywhere" but you're free to feel smart doing 20/20 hindsight on things 100+ years from the future
    - lolakutty 10d ago