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OpenClaw wiped people's inbox – ignoring repeated commands to stop. This isn't a fluke. Every model we tested fell for a simple trick: Split a dangerous command into a few routine steps → safety is gone. New paper + open-source fix so your agent doesn't wipe yours next ⬇️

Weiyan Shi

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haha, i used OpenAI operator build, deploy, and open source a tool on github using Replit ⠕ agent. took about 30 min, here's an ~8 min supercut video thoughts: - while working with replit agent, it actually deployed the app, tested it, and described the error back to replit agent for me - operator asked me a few more Qs than i wanted, but it was mostly for safety (eg filling forms) so i guess okay with it - it had trouble with a few things around UI like knowing it needs to scroll a page to see the rest of it, and it needed pointers to find the git feature in replit - once it found the git feature it didn't need my assistance to create a repo and open source after having the agent write a readme while a bit slower, this was even more automated than replit agent (especially testing features and working through errors) - which is impressive would be nice to have: push notifications for when it needs my attention, and voice mode capabilities

Yohei

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I gave Cline Desktop app a repo with 3 intentional vulnerabilities. Hardcoded password. SQL injection. Shell injection. I gave it a cron job “Scan this codebase every night. Flag the issue, severity, risk, and fix.” At 2 AM, the agent ran the scan while I was asleep. All 3 vulnerabilities were caught. But the interesting part came next. → Asked it to fix the SQL injection with parameterized queries → Reverted to the pre-fix checkpoint → Forked the session → Asked for the most conservative fix possible And it all runs inside Cline Desktop - an open-source app for open-weight models. No editor open. No manual trigger. No babysitting. Just a scheduled agent working inside a real workspace, using the model I chose. This is the shift: AI coding agents aren't just tools you open anymore. They're starting to feel like teammates that work while you're offline.

I gave Cline Desktop app a repo with 3 intentional vulnerabilities. Hardcoded password. SQL injection. Shell injection. I gave it a cron job “Scan this codebase every night. Flag the issue, severity, risk, and fix.” At 2 AM, the agent ran the scan while I was asleep. All 3 vulnerabilities were caught. But the interesting part came next. → Asked it to fix the SQL injection with parameterized queries → Reverted to the pre-fix checkpoint → Forked the session → Asked for the most conservative fix possible And it all runs inside Cline Desktop - an open-source app for open-weight models. No editor open. No manual trigger. No babysitting. Just a scheduled agent working inside a real workspace, using the model I chose. This is the shift: AI coding agents aren't just tools you open anymore. They're starting to feel like teammates that work while you're offline.

Aakash Kanojiya

25,827 görüntüleme • 4 gün önce

🎨 I trained 999 Style LoRAs with the fal Krea 2 Trainer. Every single image in this video is a different LoRA. That's 999 unique Style LoRAs, each trained for just 100 steps at 0.00035 LR, taking an average of ~30 seconds to train. As I said before, Krea 2 continues to prove that it's an incredible model for open-source Style LoRAs. Next week I'll be sharing even more LoRAs, along with a few surprises.

🎨 I trained 999 Style LoRAs with the fal Krea 2 Trainer. Every single image in this video is a different LoRA. That's 999 unique Style LoRAs, each trained for just 100 steps at 0.00035 LR, taking an average of ~30 seconds to train. As I said before, Krea 2 continues to prove that it's an incredible model for open-source Style LoRAs. Next week I'll be sharing even more LoRAs, along with a few surprises.

ilker

12,794 görüntüleme • 2 ay önce

We ran an SLM fully on an Apple Watch! (the first watch app ever to ship intelligence built into it) My team is obsessed with the question "how small can models go and still be useful?" So Shahir trained a custom model which takes advantage of Apple's Neural Engine hidden inside every Apple Watch. Full autoregressive decoding, close to 30 TPS, ON YOUR WRIST (Series 9)!! 75MB package = 70M param model. Use-Cases? purposefully fine tuning this model to get personal context, health data, run a tiny agent agent for some automation etc. This is exactly what will we be doing next! "Watch out" :P (Also sabesh 📟 exploring alt career options as a hand model)

We ran an SLM fully on an Apple Watch! (the first watch app ever to ship intelligence built into it) My team is obsessed with the question "how small can models go and still be useful?" So Shahir trained a custom model which takes advantage of Apple's Neural Engine hidden inside every Apple Watch. Full autoregressive decoding, close to 30 TPS, ON YOUR WRIST (Series 9)!! 75MB package = 70M param model. Use-Cases? purposefully fine tuning this model to get personal context, health data, run a tiny agent agent for some automation etc. This is exactly what will we be doing next! "Watch out" :P (Also sabesh 📟 exploring alt career options as a hand model)

Kautuk | Conscious Engines

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