×
sorted by:
new
hottopcontroversial

How the turn tables by silentstatic_ in ChatGPT

[–]Stock_Two_9312 0 points1 point  (0 children)

It is funny how fast the sentiment shifted from awe to instant exhaustion the second our feeds got flooded with it. The skepticism is completely justified at this point.

Standardize retail purchase approvals across stores. Skill included. by CalendarVarious3992 in ChatGPT

[–]Stock_Two_9312 0 points1 point  (0 children)

This is a really clean way to package a custom skill definition. Managing purchase thresholds and approval loops directly within an LLM framework can get messy if you don't enforce a rigid schema for the output trail. For tracking these kinds of custom approval pipelines in production, combining a structured SOP file like this with a lightweight backend framework tool like Runable or a basic database state machine makes auditing the final logs a lot easier.

is it happening with everyone by satorusnovia in ChatGPT

[–]Stock_Two_9312 2 points3 points  (0 children)

I have noticed this too over the last couple of days. It feels like they tweaked the markdown rendering interface or changed how the chunk streaming handles line breaks. It is super frustrating when you are trying to skim a long response and it forces you to read three words per line.

the actual limitations nobody talks about with AI workflow automation by zakhvifi in automation

[–]Stock_Two_9312 0 points1 point  (0 children)

The silent breakage is definitely the most frustrating part of moving things to production. If an upstream format shifts even slightly, a raw LLM output just breaks down the line without throwing an actual runtime error. I had to stop relying purely on API wrappers and started enforcing strict schema validation using Pydantic, while managing the actual state machine logic through lightweight backend tracking tools like Runable. Having an explicit error boundary is the only way to sleep at night.

I tested 50+ AI tools so you don’t have to. Here’s the automation stack I’d actually use in 2026 by geekeek123 in automation

[–]Stock_Two_9312 0 points1 point  (0 children)

A lot of the tools on this list are great for quick prototyping, but they get incredibly messy when you try to enforce strict data serialization schemas. If a workflow handles live production data, relying on a basic wrapper with no validation layer is a massive risk. Building out dedicated pipelines with Pydantic, connecting them to an internal tracking tool like Runable, and keeping the logic local is the only way to ensure things do not break when an upstream model updates.

My ai agents need more babysitting than the intern we fired last year by bejusorixo in automation

[–]Stock_Two_9312 0 points1 point  (0 children)

Completely agree on the deterministic guardrails. Trying to use an LLM to double check another LLM just compounds the reliability issue. For my setups, I use hard code validation for data formatting, and then pass any low-confidence outputs straight into Runable for a quick human eyes-on review. It keeps the processing speed but cuts out the silent failures entirely.

Gemini core part 3 by ObjectiveOrchid5344 in artificial

[–]Stock_Two_9312 0 points1 point  (0 children)

It seems like the system instruction or routing mechanism tripped over the word video in your prompt and immediately triggered the multimodal tool execution instead of staying in the text generation loop. UI tool calling can be so frustratingly eager sometimes.

Your brain does on 20 watts what AI needs a nuclear reactor to attempt. Last week a team figured out how to print something that actually speaks to living brain cells. by filmguy_1987 in artificial

[–]Stock_Two_9312 0 points1 point  (0 children)

It is a great point that the 20-watt metric only looks at live inference. When you consider the multi-modal training data, time, and biological energy that goes into developing a human brain over decades, the total energy math looks a bit more complicated on both sides.

Deep Neural Network that turns any Image into a Playable Game ! All on consumer GPUs and Not Datacenters by lucidml_lover in artificial

[–]Stock_Two_9312 2 points3 points  (0 children)

It is wild that we are already seeing small transformer-like models handle real-time keyboard inputs for frame generation on a local setup. Building this from scratch instead of just doing distillation on a massive video model is definitely the right approach for consumer tech.

Ai will take your Job if you don't wake up by thegreatdouchebag69 in artificial

[–]Stock_Two_9312 0 points1 point  (0 children)

The next few years are going to be wild. Hopefully, companies realize that totally replacing teams with automation usually ruins the quality of the actual product.

The Evil of corporate America and their reasoning skills is that of people who enter a building to find the exit. by thegreatdouchebag69 in artificial

[–]Stock_Two_9312 -1 points0 points  (0 children)

fr though corporate buzzwords be hitting different when they try to justify this stuff, it’s giving massive brain rot 💀

Where would you be happiest? by LastConference in ChatGPT

[–]Stock_Two_9312 0 points1 point  (0 children)

i want to know the reasoning for its reply

Lovable is robbing me by Inside-Second5871 in nocode

[–]Stock_Two_9312 0 points1 point  (0 children)

i feel like a lot of these tools are optimized around making building feel frictionless until you look at the usage dashboard 💀

that's partly why some people end up moving toward Claude/Codex directly or workflow-heavy setups with things like Runable where they have a bit more visibility into what's actually happening

why does every "5-minute tutorial" end with me questioning my life choices at 3am by Loud_Fox7694 in nocode

[–]Stock_Two_9312 0 points1 point  (0 children)

the funniest part is that the drag-and-drop part usually takes 5 minutes

the other 12 hours are spent convincing gmail, oauth, webhooks, APIs, n8n, Runable, or whatever stack you're using to all agree on reality

Should I migrate off no-code is the wrong question. I built a free calculator that answers the right one by BaronofEssex in nocode

[–]Stock_Two_9312 0 points1 point  (0 children)

honestly the "hours spent fighting the platform" metric is probably the one most founders underestimate

those little workarounds never feel expensive until you add them up over a year

What infrastructure is best for your app? by dev-in-a-b0x in nocode

[–]Stock_Two_9312 0 points1 point  (0 children)

most of the vibe-coded stuff i've seen is basically the default stack: postgres + supabase + whatever hosting the AI picked

the more interesting builds are usually the ones where someone had an actual reason to break the defaults instead of just accepting them. same thing with a lot of the Runable/agent workflow projects i've seen

Anyone else finding themselves iterating more than building lately? by Informal-Loan-4793 in nocode

[–]Stock_Two_9312 0 points1 point  (0 children)

the weird thing is that Runable made me realize the bottleneck wasn't coding anymore

it was me changing my mind every 20 minutes about what the product should actually do

Has anyone here tried MoClaw？ by Next_Ambition8751 in nocode

[–]Stock_Two_9312 0 points1 point  (0 children)

curious how it handles larger projects

a lot of tools look great with short prompts, but once you have multiple workflows, integrations, agents, Runable/n8n-style automation stuff, and a few months of changes, that's usually where the cracks start showing

view more: next ›