Egor

Startup founder & investor.
Building & running projects.

[Egor @EgorJioo]

Claude is already inside classified Pentagon AI workflows, and an AI hallucination reportedly came close to triggering a US-China military confrontation

A Pentagon intelligence report claimed a Chinese ship was carrying nuclear-related components to a third country

The US military began preparing to intercept it, including deploying personnel and aircraft, before officers reviewed the intelligence and discovered the AI-generated conclusion was wrong

That is the part people should pay attention to

The failure was not some conscious AI trying to start a war

It was a model producing a convincing false conclusion inside a system where the output could influence real military decisions

Claude is already being used across classified US defense and intelligence workflows through partners including Palantir, although public reporting has not established that Claude caused this specific incident

The scary part about military AI is not that it becomes Terminator

It is that one believable hallucination can move faster than the humans responsible for checking it

[Egor @EgorJioo]

Claude Opus 5 reportedly built a $5K-$10K agency-style Three.js site in two hours

The result had mouse-tracked parallax, cursor particles, a wireframe intro and small interactive details like a butterfly flying away on hover

Everything fit inside one HTML file under 1MB

No dev team and no weeks of revisions. Just one person working back and forth with the model until the details looked right

That is the part that matters

A client can give you a rough idea, and AI can now handle a huge amount of the implementation work that used to require a small studio

The bottleneck is shifting away from writing every line yourself

It is becoming much more about taste, direction and knowing exactly what to ask the model to build

[Egor @EgorJioo]

LM Studio just added fully local realtime voice transcription

You can now talk to an AI agent without sending a single byte of your voice to the cloud

Speech is processed directly on your machine, then converted to text in real time for local models and agent workflows

No external servers and no cloud API required

The feature is available on Apple Silicon Macs and NVIDIA-powered Windows and Linux machines, with AMD support reportedly on the way

This fills one of the biggest gaps in fully local AI

You could already run the model, memory and tools on your own hardware. Now the voice interface can stay local too

That gets us much closer to AI agents that can listen, think and act without your data ever leaving the device

[Egor @EgorJioo]

A new open-source Skill can look at your app and tell you how to monetize it

HaimingDev just released haiming-app-monetization, and it reached 168 stars in two days

The Skill reads your project, researches how similar products are priced, then builds a monetization plan around onboarding, paywalls and subscription tiers

The output is structured so Claude Code, Codex or Cursor can actually implement it

The best part is how conservative it is

By default, it only evaluates the project and does not touch the code. If pricing cannot be verified, it marks it as unverified instead of inventing numbers

For AI apps with ongoing inference costs, it also avoids blindly recommending unlimited lifetime plans

And the acceptance docs explicitly say it cannot promise conversion rates

That restraint makes it more useful than another AI tool promising perfect monetization

Install it with:

npx skills add HammingDev/haiming-app-monetization

[Egor @EgorJioo]

Claude Fable 5 and GPT-5.6 Sol were asked to draw Messi in a browser

Both started by finding the eyes

The prompt was just one line:

“Create a single HTML file that animates a graphite portrait of Messi being drawn, stroke by stroke”

No drawing process was specified

GPT-5.6 Sol still followed a sequence that looks remarkably close to how a portrait artist would work: block the silhouette, define the profile, find the eyes, build the hair, shape the shirt, then finish with graphite passes

That order was never written in the prompt

The model inferred the process from the task itself

That is the interesting part

It is not only generating the final face

It is reproducing the workflow of someone sitting at an easel and deciding what to draw first

[Egor @EgorJioo]

@marfinxx the governance gate belongs before execution, not after audit

[Egor @EgorJioo]

@N01ennn replay turns one expensive run into a reusable search asset

[Egor @EgorJioo]

Claude Fable 5 and GPT-5.6 Sol were asked to draw Messi in a browser

Both started by finding the eyes

The prompt was just one line:

“Create a single HTML file that animates a graphite portrait of Messi being drawn, stroke by stroke”

No drawing process was specified

GPT-5.6 Sol still followed a sequence that looks remarkably close to how a portrait artist would work: block the silhouette, define the profile, find the eyes, build the hair, shape the shirt, then finish with graphite passes

That order was never written in the prompt

The model inferred the process from the task itself

That is the interesting part

It is not only generating the final face

It is reproducing the workflow of someone sitting at an easel and deciding what to draw first

[Egor @EgorJioo]

Claude Opus 5 reportedly built a $5K-$10K agency-style Three.js site in two hours

The result had mouse-tracked parallax, cursor particles, a wireframe intro and small interactive details like a butterfly flying away on hover

Everything fit inside one HTML file under 1MB

No dev team and no weeks of revisions. Just one person working back and forth with the model until the details looked right

That is the part that matters

A client can give you a rough idea, and AI can now handle a huge amount of the implementation work that used to require a small studio

The bottleneck is shifting away from writing every line yourself

It is becoming much more about taste, direction and knowing exactly what to ask the model to build

[Egor @EgorJioo]

@Shred_0x hands as primary contact lets steering inputs help manage balance

[Egor @EgorJioo]

@AlgoKavya execution rules should account for trade frequency, not spread alone

[Egor @EgorJioo]

@0x_fokki loop first, then codify stable work into reliable automation

[Egor @EgorJioo]

@gippp69 verification and approval can stay consistent across model swaps

[Egor @EgorJioo]

@slash1sol @LuelCompany mcp access makes dataset licensing composable with agent workflows

[Egor @EgorJioo]

@kocer_eth confidence scores need provenance before they can support action

[Egor @EgorJioo]

@monokern fewer agents matter here because context stays inspectable

[Egor @EgorJioo]

@Slonski_rt plugin review keeps community growth manageable

[Egor @EgorJioo]

@x_insider4 one clean hit does not establish that the regime logic generalizes

[Egor @EgorJioo]

@beamnxw open-source components still need orchestration to work as one stack

[Egor @EgorJioo]

@Damir_Akaza quiet bridge nodes deserve structural checks, not just alert triage

[Egor @EgorJioo]

@cryptocompikbro a 626-prediction record still cannot hedge a single series result

[Egor @EgorJioo]

@eng_khairallah1 without forgetting, persistent memory can preserve contradictions

[Egor @EgorJioo]

@slash1sol low token cost still needs a practical workflow behind it