akssassin907

5 ms·

1.
by akssassin907 7mo ago
The context creep point really resonates. it's easy to keep adding 'just one more thing' to a prompt without realizing each run is getting heavier. I hatched three lobsters and sent them on random errands, $250 vanished. One
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by akssassin907 7mo ago
Nice to see a real-world example of Codex handling a port like this. The browser first approach makes sense for TTS, being able to run it locally without a server is a big deal for privacy. Curious how the output quality compares to the ori
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by akssassin907 7mo ago
This has been building for a while. The gap between 'AI safety' as a company value and real-world government contracts was always going to create friction. Still, it's hard to believe the US government's own AI capabilit
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by akssassin907 7mo ago
Always felt like the 'stochastic parrot' argument was missing something but couldn't articulate why. This article puts it into words nicely.
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by akssassin907 7mo ago
Really appreciate that this bakes in PII redaction and a review step before anything is published. A lot of tools that push data somewhere skip that entirely. The 'export locally first, review, then publish' flow seems like the ri
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by akssassin907 7mo ago
The part that gets me is 24,000 fake accounts and 16 million API calls. That's not a side project, that's a coordinated operation. At some point "distillation" stops being a technical term and starts being a very polite
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by akssassin907 7mo ago
Running local LLMs on Apple Silicon has gotten surprisingly capable — the M-series chips handle models that used to require expensive GPU setups, so tools like this that actually speak to that hardware are welcome. The quantization comparis
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by akssassin907 7mo ago
The architecture here is right, local execution, remote window. Running agents on your own machine means your filesystem, tools, and env stay intact, you're not paying cloud rates for compute that your hardware can already handle. The
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by akssassin907 7mo ago
I'm a technology proponent, but tech doesn't replace a human teacher when it's 24 students, 1 teacher, and the lesson plan is "open your i-Ready modules." My 4 kids all use i-Ready. It has real value for reinforcing
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by akssassin907 7mo ago
The buried insight is right: if random keystrokes produce playable games, the input is basically noise and the system is doing all the work. We've evolved past the point where intent matters. That's either the most exciting or mos
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by akssassin907 7mo ago
By Moore's Law a 4-year-old machine should be a quarter of what's available today, it should be struggling. Instead it's rocking. Either Moore's Law is stalling, or software efficiency is finally catching up. Either way,
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by akssassin907 7mo ago
This is exactly the kind of thing teams need. The "just share your API key" shortcut is everywhere and it always comes back to bite you. Nice work solving a real problem. It's exciting watching people figure this stuff out an
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by akssassin907 7mo ago
Fair point, I was going off the "quoted suspension email" in the OP. If it's only the AI service and not the full account, that is definitely not as bad. Either way the zero-warning policy though is a big issue IMP.
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by akssassin907 7mo ago
The heuristic detection approach is fine. The penalty ladder is broken. Reasonable progression: warning email → quota throttle → AI Pro subscription suspended → Google account suspended. They skipped to step 4 on a first offense, paid accou
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by akssassin907 7mo ago
Fine, restrict the OpenClaw usage. Fine, cancel the AI Pro subscription. But nuking Gmail, Google Photos, Drive — years of irreplaceable personal data — as punishment for how you routed tokens? That's not enforcement, that's colle
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by akssassin907 7mo ago
The buffet analogy breaks down here. Using OpenClaw isn't stuffing steaks in your bag — you're eating the same food, in the same seat, consuming the same tokens your subscription allows. Google banned you because they didn't
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by akssassin907 7mo ago
Totally valid for fixed, well-defined tasks — a cron job is cheaper and more reliable there. The LLM earns its keep when the heartbeat involves contextual judgment: not just "is there a task in the queue" but "given everythin
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by akssassin907 7mo ago
That's a cleaner implementation than what I described. Small model as meta-router: classify locally, escalate only when confidence is low. The self-evaluation loop you're suggesting would add a quality layer without much overhead
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by akssassin907 7mo ago
The pattern I found that works ,use a small local model (llama 3b via Ollama, takes only about 2GB) for heartbeat checks — it just needs to answer 'is there anything urgent?' which is a yes/no classification task, not a fron
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by akssassin907 7mo ago
This is where the Claw layer helps — rather than hoping the agent handles the interruption gracefully, you design explicit human approval gates into the execution loop. The Claw pauses, surfaces the 2FA prompt, waits for input, then resumes
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by akssassin907 7mo ago
That's actually a cleaner editorial standard than most publications follow. The major risk in tech journalism isn't disclosed sponsorships — it's the undisclosed access journalism where coverage tone shifts to maintain relati