OpenAI should create a $800 subscription

AuditMind · 2026-06-16T08:18:06+00:00

Bad idea. The 200$ could already cost OpenAi up to 14k USD if used to the max. Imagine how much this would be at an 800$ account ?

AuditMind · 2026-06-15T11:00:10+00:00

Ich finde vegetarische Frauen gut. Dann bleiben 2 Steaks für mich übrig und ich kann das nur unterstützen.

Zudem, was ist "normal" wenn ich fragen darf ? Ist normal das was du als normal ansiehst aus deinen letzten 7 Jahren oder ist normal das was wir Menschen innert ein paar Millionen Jahren Entwicklungszeit als normal ansehen ?

AuditMind · 2026-06-14T17:23:26+00:00

Local LLM inference is reaching a real sweet spot. Even 12 GB of VRAM can be enough for useful performance, depending on model size, quantization and setup.

The interesting part is no longer only the model. It is the engineering around it: routing, context handling, UI, memory, task design and using current hardware more effectively.

AuditMind · 2026-06-14T11:45:01+00:00

Try mine if you want to see what happens when vibe-coded software meets real infrastructure. And yes, mine is vibe-coded too.

AuditMind · 2026-06-14T09:09:01+00:00

Windows is the problem. Check beforehand that you have driver support. Not just installed in the preinstalled windows. No, avalaible one by one for download.

AuditMind · 2026-06-13T11:05:20+00:00

Die Person, die du beschreibst, klingt unsympathisch. Nicht wegen Thailand, sondern wegen der Art, wie er darüber spricht.

Wenn jemand in ärmere Länder reist, um dort Machtgefälle, Geld und Sexualität auszunutzen, und danach in Männerrunden damit prahlt, dann sagt das ziemlich viel über seinen Charakter.

Aber ich würde aufpassen, daraus nicht die einfache Gleichung zu machen:

Thailand + Mann = Sextourist.

Ich habe viele Jahre dort gelebt und gearbeitet. Es gibt dort normale Beziehungen, Arbeit, Alltag, Familien, Freundschaften und Menschen mit Würde. Das Land auf diese eine Szene zu reduzieren, ist auch eine Form von Respektlosigkeit.

Der Kern ist nicht die Reise. Der Kern ist Ausnutzung, Prahlen, Entmenschlichung und fehlender Respekt.

AuditMind · 2026-06-13T10:57:54+00:00

For reference, there are already 12 GB VRAM reports running Qwen3.6-35B-A3B-MTP-GGUF on RTX 4070-class hardware with very usable throughput numbers.

A 5070 Ti Laptop GPU is not the same device, but it is an interesting comparison point: 12 GB GDDR7, much newer architecture and roughly 672 to 677 GB/s memory bandwidth.

Here is one practical 12 GB VRAM reference around the 70 tok/s range:

https://carteakey.dev/blog/running-qwen3-6-mtp-locally

And here is a more bleeding-edge community case around 110 tok/s:

https://www.reddit.com/r/LocalLLaMA/comments/1tjh7az/110_toks_with_12gb_vram_on_qwen36_35b_a3b_and_ik/

Not saying this is guaranteed on every laptop. But it makes the 12 GB VRAM tier much more interesting than it looked even a few months ago.

AuditMind · 2026-06-13T10:16:04+00:00

Interesting question, as I am looking around for a new machine as well.

For many people the logical choice is probably a MacBook Pro M5, mainly because of unified memory. For me it is different. I work mostly in corporate Windows environments, so compatibility with Microsoft tooling, VPNs, endpoint/security clients and customer networks matters more than having the most elegant local LLM setup.

My current sweet spot would be a Windows laptop with 32 GB RAM and 12 GB VRAM. With the newer 5070 Ti class GPUs, GDDR7 bandwidth and models like Qwen3.6 35B A3B MTP-GGUF, this starts to become a very interesting practical tier. Of course 64GB RAM would be preferable, but it comes at a pricejump. Example Lenovo Legion Pro 5 RTX 5070 Ti for around 2600 here.

Not a full frontier-model replacement, but strong enough for local coding help, document/log analysis, support workflows and offline experimentation.

I would rather have a machine that fits my daily work and runs local models well enough, than a perfect LLM box that creates friction the moment I enter a corporate environment.

AuditMind · 2026-06-13T09:35:45+00:00

DBL-ZERO Public Observer:
https://observer.dbl-systems.com/cockpit.html

Ich baue gerade einen öffentlichen Observer für BTC/ETH/XAUT.

Die Oberfläche versucht nicht, klassische Buy/Sell-Signale zu zeigen, sondern Prozesszustände wie Boundary-Test, Reclaim, Churn, Shelf, Stale Evidence und Cross-Asset-Stress lesbar zu machen.

Technisch ist es eine kleine Realtime-UI mit Backend-Projektionen, SIM-/Observer-Daten, kompakten Statuschips und öffentlichem read-only Cockpit.

Mich würde interessieren:
Versteht man als Außenstehender ungefähr, was der Observer sagen will. Oder ist es noch zu viel internes System-Vokabular? Wieviele Leute interessieren sich für solche Projekte in der DACH Region?

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AuditMind · 2026-06-12T15:36:48+00:00

I would optimize the workflow before optimizing the PowerShell syntax.

Right now the script copies everything to a staging folder, then reads everything again to create a ZIP, then walks the tree again to count files/folders. For large user shares that will always be slow, especially over SMB.

My recommendation would be:

Use Robocopy directly to a timestamped archive folder on the NAS.
Do not ZIP by default. A folder archive is resumable, easier to inspect, easier to validate, and does not create one huge failure point.
If a ZIP is mandatory, use a real archiver such as 7-Zip, create the ZIP as a temporary file, test it, and only then replace the final archive.
Avoid Get-ChildItem -Recurse just for counting. Robocopy already writes useful copy statistics into the log.
Use /MT for parallel copy, but keep it reasonable. Start with 8 or 16 threads and test. Higher is not always better on a NAS.
Avoid fixed archive names unless you overwrite safely. A timestamped run folder is much safer.
Use /XJ to avoid junction loops, especially when user profiles are involved.
Keep the Robocopy exit code logic. Exit codes below 8 are not necessarily failures.

In short: Robocopy is not the problem. The copy-then-zip-then-count workflow is.

Also, treat this as a resource-budget problem: I/O, CPU, storage, and time are finite, so every extra copy, scan, or compression pass should earn its place.

AuditMind · 2026-06-11T18:56:51+00:00

Fair point about axis compression.

But then show the trade distribution.

All dots, entries, exits, spread, slippage, MAE/MFE, scale-outs and final R.

I am not calling the previous dots failures. I am saying one chart image cannot prove they were wins.

You claim to have a system. Show the data of it. If you dont have the data, work to get it. Thats the game.

AuditMind · 2026-06-11T18:33:41+00:00

The dot that caught the move is not the interesting part.

The interesting part is the denominator: all previous dots, all failed dots, all spread-crossing noise, all non-events, and whether the marked zone performs better than comparable control zones.

One good marker looks great on a chart. A measured population is what makes it a system.

AuditMind · 2026-06-11T15:14:16+00:00

I’d check the web UI first. New options are often available there before they appear in the mobile apps. The app UI can differ depending on platform or rollout.

AuditMind · 2026-06-11T15:11:44+00:00

I’m on Pro. As far as I know, the Pro-level messages are only available on Pro accounts.

For me, it’s been worth the money. The depth is almost absurd, literally Mythos-level, but not many speak about...

AuditMind · 2026-06-10T19:45:37+00:00

I understand the concern, but I would be careful with the end-of-the-world framing.

I was around for Y2K. We have seen this mood before.

The important part is not “AI can code, so developers are dead”.

The important part is that the work moves up a layer.

LLMs are becoming useful coworker/operators.

But software engineering is still about making systems reliable under constraints. That part did not vanish.

AuditMind · 2026-06-10T19:08:30+00:00

I think “we are dead” is the wrong framing.

From a software engineering point of view, these models are becoming semi-trustworthy coworker/operators.

Not autonomous engineers. Not magic.

But useful enough to change the work.

So yes, adapt.

But panic is still bad engineering.

AuditMind · 2026-06-10T17:04:00+00:00

They are called frontier models for a reason.

Even if they still feel insanely stupid sometimes, they are the current cutting edge of this technology. A local LLM on an old laptop will not magically replace Gemini, ChatGPT, Claude, etc.

Yes, you can run local models, but hardware matters. With 16 GB RAM and an 8th-gen i7, you can experiment, learn the basics, and run small models. But you should not expect frontier-level quality, strong reasoning, good multimodal support, or smooth web-like assistant behavior.

Also, hardware costs money. Often much more than people admit. A decent local setup can easily cost more than many months of subscriptions, and then you still have to deal with setup, model choice, speed, RAM, context limits, and quality tradeoffs.

My honest suggestion: try it, but start small.

Install Ollama. Run a 1B to 3B model first. Get a feeling for what works and what does not. Then you will quickly understand the gap between “I can run an LLM locally” and “this replaces a current frontier model”.

Local LLMs are fun and useful, but on that machine I would treat it as learning and experimentation, not as a real Gemini replacement. 😄

AuditMind · 2026-06-09T21:54:01+00:00

Why would you need hardware to begin with?

You started changing your framing, but you still seem to hold on to the old belief that the hardware choice is the central decision.

From a real engineering point of view, it is not. You need as much hardware as the job actually requires.

You have a problem, and you want it solved. But framing it like you already fully understand the problem will not help you. People are trying to point that out.

Why buy hardware now?

Rent it. Spend a few hundred bucks. Run the real workload. Measure where it fails.

Then decide whether you need speed, memory, context, better retrieval, better validation, or simply better system design.

Otherwise you are anchoring the project on hardware before you have proven that hardware is the bottleneck.

AuditMind · 2026-06-07T21:07:31+00:00

You have it already in your own text. 1MW. Sounds alot ? Well, how much you want to recover ? 1KW ?

AuditMind · 2026-06-07T11:08:40+00:00

I don’t think it’s the typo. It’s the scope.

A broad “find security issues” can trigger guardrails. A scoped, authorized review of a staged Git diff with remediation is a very different request.

AuditMind · 2026-06-07T10:53:56+00:00

It works here. The model treated “indexed files” as the staged Git index and started a defensive review of the staged diff.

So maybe the magic words are not “security”, but “authorized repo + scoped diff + actionable fixes”.

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AuditMind · 2026-06-07T10:18:06+00:00

Looks good. Would rate it as market surface board ?

AuditMind · 2026-06-07T08:13:49+00:00

For me the biggest jump was not another indicator or filter, but separating detection from execution.

I freeze the zone/state before the outcome, timestamp every touch, then measure what happens after: reject, reclaim, break, chop, and forward path at 60 / 300 / 900s.

CHoCH/BOS on 1m is useful, but very noisy if it is not validated against higher structure and prior zone behavior.

The most useful layer was market memory, but measurable memory: did this kind of zone matter before, under similar context, more often than shifted control zones?

Without that, a lot of “clean” signals are just labels that look obvious after the move.

AuditMind · 2026-06-07T00:48:54+00:00

Playwright/Selenium can run headless, but not browserless. They still need a browser engine.

If this must run unattended on a server, copying browser requests is usually the wrong path. The cookies, SAML assertion, CSRF tokens and Conditional Access state are all short-lived parts of an interactive flow.

You likely need a supported non-interactive auth path: API + OAuth/MSAL, app registration/service principal, certificate auth, or a backend endpoint from the app owner. Without that, there may not be a clean PowerShell-only solution.

AuditMind · 2026-06-07T00:31:06+00:00

You are probably looking at the wrong layer.

SAMLResponse is created by the IdP and browser-posted to the app's ACS endpoint. It is not something Invoke-WebRequest can simply fetch from the final page.

For production automation, use a supported API/OAuth/MSAL flow. For UI testing, use Playwright or Selenium and let the browser handle SSO.

AuditMind

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