the problem: i pay for claude code, codex AND cursor. still, every few hours one of them hits its usage limit right in the middle of a task. then it's open another terminal, re-explain everything to the next agent, lose all the context. felt dumb paying for 3 plans and still getting blocked.
I noticed GPT-6 Astra was good at identifying when it had finished a phase and no longer needed the raw tool output, so I built a Codex patch that lets it request compaction itself.
The model writes a checkpoint with the relevant decisions, constraints and next steps. The patch runs native compaction, carries that checkpoint forward verbatim, and resumes the task.
The idea for this project started out of sheer frustration. As I was building my own AI wrappers, I kept hitting the same wall: AI unit economics are hard.
API prices change every week, token counting is confusing, and calculating if a feature is actually profitable often feels like guesswork. I got tired of messy spreadsheets, so I built a suite of tools to solve it.
I'm building an AI tool that helps HubSpot sales teams save time by cutting down the manual work after sales emails and customer conversations.
It is an AI layer that would be used as a HubSpot sidecard (extension). It can connect to your Gmail and can read sales emails, filter out noise, detect new leads vs follow-ups and turn each one into an approval card showing the extracted info (name, email, phone, company, position, deal-value warm/cold score) with source sentence, summary and next steps, you can approve/reject it on a field level and when you save the contact into hubspot it also automatically saves notes with user in HubSpot and creates reminders. it flags the promises made, silent deals and gives daily summary.
I'm still developing so please tell me what you think about the idea and how can I improve it if needed ?
I m thrilled to announce that SoloPlate will launch tomorrow, an AI-powered meal assistant built to kill "Decision Fatigue." I created this using @Base44after realizing that for many of us, the hardest part of cooking isn't what you cook with, it's figuring out what to make. After a long day of work, the last thing you want to do is have to do more work.
SoloPlate is designed to be your digital sous-chef.
Decision-Free: AI helps you pick the perfect meal instantly.
Beginner-Friendly: Simple instructions for those new to the kitchen.
Watch & Learn: A dedicated video section for those who prefer visual guidance.
On Oct 15, I launched The CLEAR Funnel Playbook (a system helping software teams discover and govern hidden AI tools) with literally zero audience. No followers, no email list, no hunter.
Results: #417, 0 points, 2 comments.
Honestly? It was the best thing that could've happened. Here's what I learned:
I'm building Olly, an AI career copilot, and one problem keeps coming up in conversations with job seekers: most people prepare hard but have no real way to tell if they're ready for the role they're targeting.
Practice questions and mock interviews help, but at the end you're usually left with a vague feeling of "I think I did okay."
I'd love to hear how others handle this:
- How do you judge your own readiness before an interview?
Over the past few months, we ve been working on BetterCause between university classes and late-night coding sessions. The idea came from our own struggle understanding food products for our own diet.
Trying to figure out if a packaged product actually fits your diet or health goals usually means having to understand labels and searching up chemical additive names on your phone.
