11–20 of 85 posts
So is "AI Agents" something the community has settled on or is this a Google-ism? I remember people arguing about this some time ago with no definitive answer.
I think this post underestimates how the degree to which “what data is correct” is deeply contextual. My team created an identical hypothesis to this doc ~2 years ago and generated a proof of concept. It was pretty magic, we had fortune 500 execs asking for reports on internal metrics and they’d generate in a couple of minutes. First week we got rave reviews - followed by an immediate round of negative feedback as we…
We've looked at using agents at my current job but most of the time, once the data is properly structured, a more traditional approach is faster and less expensive.
I think this post underestimates how the degree to which “what data is correct” is deeply contextual. My team created an identical hypothesis to this doc ~2 years ago and generated a proof of concept. It was pretty magic, we had fortune 500 execs asking for reports on internal metrics and they’d generate in a couple of minutes. First week we got rave reviews - followed by an immediate round of negative feedback as we…
Did the execs immediately recognize that the reports were wrong, or did some analyst working in a cubicle on the 9th floor point that out?
I think this post underestimates how the degree to which “what data is correct” is deeply contextual. My team created an identical hypothesis to this doc ~2 years ago and generated a proof of concept. It was pretty magic, we had fortune 500 execs asking for reports on internal metrics and they’d generate in a couple of minutes. First week we got rave reviews - followed by an immediate round of negative feedback as we…
Did the execs immediately recognize that the reports were wrong, or did some analyst working in a cubicle on the 9th floor point that out?
So let me get this straight- we are going to train AI models to perform screen recognition of some kind (so it can ascertain layout and detect the "important" ui elements), and additionally ask that AI to OCR all text on the screen so it has some hope of being able to follow some natural language instructions (OCR being a task which, as a HN thread a day or two ago pointed out, AI is exceedingly bad at), and then we're going to be able to tell this non-deterministic prediction engine what we want to do with our software, and it's just going to do it?
Like Homer Simpson's button pressing birdie toy? :smackshead:
Why do I have reservations about letting a non-deterministic AI agent run my software?
Why not expose hooks in some common format for our software to perform common tasks? We could call it an "application programming interface". We might even insist on some kind of common data interchange format. I hear all the cool people are into EBCDIC nowadays.
Then we could build a robust and deterministic tool to automate our workflows. It could even pass structured data between unrelated applications in a secure manner. Then we could be sure that the AI Agent will hit the "save the world" button instead of the "kill all humans" button 100% of the time.
On a serious note, we should study various macro recording implementations, to at least have a baseline of what people have been successfully doing for 40+ odd years to automate their workflows, and then come up with an idea that doesn't involve investing in a new computer, gpu, and slowly boiling the oceans.
This reeks of a solution in search of a problem. And the solution has the added benefit of being inefficient and unreliable. But, people don't get billion dollar valuations for macro recorders.
Is this what they meant by "worse is better"?
Edit: and for the love of FSM, please do not expose any new automation APIs to the network.
I think this post underestimates how the degree to which “what data is correct” is deeply contextual. My team created an identical hypothesis to this doc ~2 years ago and generated a proof of concept. It was pretty magic, we had fortune 500 execs asking for reports on internal metrics and they’d generate in a couple of minutes. First week we got rave reviews - followed by an immediate round of negative feedback as we…
>I still work on AI powered products and I don’t see even a little line of sight on this problem. Everyone’s data is immensely messy and likely to remain so.
I've worked in the space as well and completely unstructured data is better than whatever you call a database with a dozen ad hoc tables each storing information somewhat differently to each other for reports written by a dozen different people over a decade.
I have a benchmark for an agentic system which measures how many joins between tables the system can do before it goes off the rails. But there is nothing off the shelf that does it and for whatever reason no one is talking about it in the open. But there are companies working to solve it in the background - since I've worked with three so far.
Without documentation giving some grounding about what the table is doing, you're left with hoping the database is self documenting enough for the agent to figure out what the column names mean and if joining on them makes sense - good luck doing it on id1, id2, idCustomerLocal, id_customer_foreign though.
In reality, as always, I suspect the truth will be somewhere in between. SaaS products that succeed will be those that have a good UI _and_ and good API that LLMs can use.
An LLM is not always the best interface, particularly for data access. For most people, clicking a few times in the right places is preferable to having to type out (or even speak aloud) "Show me all the calls I did today", waiting for the result, having to follow up with "include the time per call and the expected deal value", etc etc.
There is undoubtedly an opportunity for disruption here, but I think an LLM only SaaS platform is going to be a very tough sell for at least the next decade.
Continuing on with my "old man yells at cloud" meme of late, here's my hot take: So let me get this straight- we are going to train AI models to perform screen recognition of some kind (so it can ascertain layout and detect the "important" ui elements), and additionally ask that AI to OCR all text on the screen so it has some hope of being able to follow some natural language instructions (OCR being a task which, as…
Continuing on with my "old man yells at cloud" meme of late, here's my hot take: So let me get this straight- we are going to train AI models to perform screen recognition of some kind (so it can ascertain layout and detect the "important" ui elements), and additionally ask that AI to OCR all text on the screen so it has some hope of being able to follow some natural language instructions (OCR being a task which, as…
AI is amazing at OCR, we've had tesseract ocr for 40 years and if you read the fine manual it has essentially a 0% error rate per character.
OCR on VLMs is terrible.
For some reason consistent x-heights between 10 to 30 pixels with guaranteed mono-column layout is not something venture capitalists get excited about, and as a result I'm not the founder of a unicorn.
Continuing on with my "old man yells at cloud" meme of late, here's my hot take: So let me get this straight- we are going to train AI models to perform screen recognition of some kind (so it can ascertain layout and detect the "important" ui elements), and additionally ask that AI to OCR all text on the screen so it has some hope of being able to follow some natural language instructions (OCR being a task which, as…
The scariest part is, as this advances, the level of disasters we're likely to see will at best be bankrupt corporations, and at worst, people being hurt/killed (depending on how carelessly these tools are integrated into mission critical systems).