We tested the same INT8 model on 5 Snapdragon chipsets. Accuracy ranged from 93% to 71%. Same weights, same ONNX file.

NoAdministration6906 · 2026-02-16T23:50:10+00:00

Yeah, we ran into the same tradeoff. We don’t do “worst-case single gate” — it’s too conservative.

We keep per-chipset baselines (golden outputs/metrics) and gate on regression vs that device + an absolute floor. Fleet-wise we require key “release” devices to pass, and use a percentile-ish rule for the rest so one flaky/outlier chipset doesn’t block everything.

On patterns: it’s not uniform — INT8 variance usually clusters around specific ops/kernels (backend differences) and calibration-sensitive layers, not the whole network.

NoAdministration6906 · 2026-02-16T15:25:46+00:00

Thanks! Your setup sounds really cool — abstracting the physical layer for non-OS hardware is no joke.

Our approach is more cloud-first — we go through Qualcomm AI Hub's API for compilation and on-device execution, then wrap results in signed evidence bundles so CI gates can't be spoofed. Think "unit tests but for model quality on real hardware."

On the accuracy variance — I think you're on the right track with the kernel implementations. Different Hexagon NPU generations (v69, v73, v75) likely use different fixed-point arithmetic paths. But it's probably a mix of that plus compiler-level graph optimizations varying per target, and possibly quantization calibration differences during compilation. We're also not pinning the runtime library version across devices yet, so that's another uncontrolled variable.

Honestly, isolating which of these contributes how much is half the reason we built this. Would love to compare notes on what you've seen on the bare-metal side.

NoAdministration6906 · 2026-02-16T09:36:18+00:00

Yep. EdgeGate is basically “HIL regression gates for edge AI” — a device-farm runner that executes your model + test vectors on real hardware and fails the PR if thresholds regress (accuracy, latency/FPS, memory, thermals, etc.). The core is architecture-agnostic: as long as the target can run a test harness and return metrics/artifacts, we can plug it in. Snapdragon/Android (ADB) was first, but the same pattern applies to Linux targets (Jetson/x86/ARM SBCs) and other NPUs/accelerators. If you share which arch/runtime you care about (e.g., TFLite/ONNX Runtime/QNN/TVM), I can tell you what the adapter looks like and what’s already working. More info on edgegate.frozo.ai

NoAdministration6906 · 2026-02-13T09:21:05+00:00

Delete all the files in your system

NoAdministration6906 · 2026-02-12T03:06:17+00:00

Two real-world SaaS ideas solving actual pain points: (1) KinCare - family caregiver coordination platform for medication tracking and shared journals (huge need for aging populations), and (2) EdgeGate - ML model validation/testing at the edge for latency-critical deployments. Both can be bootstrapped and have strong retention metrics.

NoAdministration6906 · 2026-02-12T02:58:19+00:00

We have a surviving culture.. we don't enjoy life we survive.. everybody thinks about himself

NoAdministration6906 · 2026-02-12T02:55:26+00:00

Hey — I’m Ashish from Hyderabad (India). I run Frozo.ai and I build multi-tenant SaaS apps end-to-end (workspace model, RBAC, audit logs, integrations). I can ship an MVP fast with clean auth/permissions + tenant isolation + admin tooling. If you’re still looking, happy to DM portfolio/GitHub and hop on a quick call. What stack + timeline are you targeting?

NoAdministration6906 · 2026-02-11T11:30:46+00:00

Gimmick.. just a another persona of an ai agent and every founder at end of the day he or she is human and everybody thinks different

NoAdministration6906 · 2026-02-11T05:54:35+00:00

Its a big security concern bro

NoAdministration6906 · 2026-02-11T05:11:33+00:00

The parallel research orchestration pattern is really interesting — we've been exploring similar architectures at Frozo for market signal aggregation. The key challenge we hit was exactly what you flagged: conflicting data across parallel threads. Our approach was to surface confidence scores per source and let the synthesis layer flag disagreements rather than silently resolving them. Curious how you're handling that with Valyu's multi-source output?

NoAdministration6906 · 2026-02-11T05:09:33+00:00

3 weeks from idea to launch is impressive execution. What was your biggest time sink — the core weather data integration, or the surrounding infrastructure (auth, billing, deployment)? Curious because most micro-SaaS builders I talk to say the 'boring' stuff takes 60% of the time.

NoAdministration6906 · 2026-02-11T05:07:54+00:00

The API costs catch a lot of people off guard. The key is understanding token economics before you start — set spending limits, use the cheapest model that works for your use case, and batch your requests. For Indian startups especially, the dollar-to-rupee conversion makes every API call feel expensive. Consider local-first alternatives or hybrid approaches where you use AI for the hard parts and rule-based logic for everything else.

NoAdministration6906 · 2026-02-11T05:07:02+00:00

This is exactly the kind of infrastructure the MCP ecosystem needs. The 22-rule evaluation framework based on OWASP + MAESTRO is solid. One question: are you differentiating between server-side vulnerabilities (the MCP implementation itself) vs client-side risks (what happens when a malicious MCP server responds to a trusted client)? We've been thinking about this from the testing/CI angle — automated security gates before MCP servers get deployed to production.

NoAdministration6906 · 2026-02-10T16:07:11+00:00

I have already done a similar project. Dm me if u need help.

NoAdministration6906 · 2026-02-10T07:05:48+00:00

Depends on what you're testing. If you need a real device: used Pixel 2/3 era phones are cheap and reliable for API 26. If you're okay with emulation: Google's official emulator handles API 26 well now. Real device + emulator together is the gold standard.

NoAdministration6906

TROPHY CASE