Bitcoin/ crypto/ gen ai/ code Building @papertradedesk
Onchain 🇺🇸🐊
Joined October 2021
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Think of a far less extreme version of the Laptop token where market makers absolutely destroyed early buyers because a small pool with a crazy starting price was supplied before the main. Init a small pool with like a 100k fdv then supply the main slightly higher. Low range liquidity added after some predefined metrics are hit. Effectively 3 different liquidity ranges. Could prob do all this without the tax
Random idea I had to disincentivize bundling:
Start the token at a higher fully diluted valuation (FDV) by locking nearly all of the supply in a single-sided Uniswap V3 or V4 range above the opening price.
This ensures that no ultra-cheap inventory is available at launch. Then attach a short-lived anti-snipe mechanism—via a V4 hook or an equivalent solution—that imposes a decaying high buy tax and per-block size caps for the first few minutes or blocks.
These measures make same-block bundling and sniping economically unattractive for everyone. Only after that window closes and on-chain metrics such as time, liquidity, or market cap are met should a locked lower-range position be added. Keep all liquidity permanently locked while still allowing fee collection.
As a result, early atomic buys gain little edge, free-floating dumpable supply is minimized, and deeper liquidity for ordinary traders appears only once real traction exists.
Written with ai
Doubt anyone will do it though since the majority of fees come pre bond. Idc to do it just a suggestion to improve the market
Random idea I had to disincentivize bundling:
Start the token at a higher fully diluted valuation (FDV) by locking nearly all of the supply in a single-sided Uniswap V3 or V4 range above the opening price.
This ensures that no ultra-cheap inventory is available at launch. Then attach a short-lived anti-snipe mechanism—via a V4 hook or an equivalent solution—that imposes a decaying high buy tax and per-block size caps for the first few minutes or blocks.
These measures make same-block bundling and sniping economically unattractive for everyone. Only after that window closes and on-chain metrics such as time, liquidity, or market cap are met should a locked lower-range position be added. Keep all liquidity permanently locked while still allowing fee collection.
As a result, early atomic buys gain little edge, free-floating dumpable supply is minimized, and deeper liquidity for ordinary traders appears only once real traction exists.
Written with ai
Agents will trade all assets onchain in time because it is so much easier to make trading agents based on blockchains. Mainly because data is all onchain and APIs have much easier and less restricted access.
You can see people starting to use different papertrade frontends with the one in first earning $117 today from 3 active traders. Cool stuff
Cole retweeted
You prompted a model in a security-sensitive environment, investigated it with forced command completions, and framed the results as evidence of a hidden backdoor.
Then you layered on geopolitical fearmongering, repeatedly emphasizing China and Beijing to imply state-sponsored espionage without proving it.
That’s not evidence of a backdoor. That’s a security observation turned into competitive and geopolitical FUD.
Publish the original prompts, full traces, and reproducible results. Let the evidence speak for itself.
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.
Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.
Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.
But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?
We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.
Read the full report: cohere.com/blog/concerning-b…
You can run lossless deepseek 4.1 flash quants on 3 dgx sparks. Still expensive (~$20,850 for 3) but far from #s you will see from people complaining. I’ve seen people say anywhere from $100k to $1.5 million, which is simply not the case. You could run it on one if you accept some perf degradation on a smaller quant. I’m not even running it locally
Some companies were spending $10k in credits per month vs hardware that has APPRECIATED. That is far more outrageous than buying $20k of hardware
Cole retweeted
This is the canary in the coal mine, y'all. The AI math doesn't math. Microsoft is offering buyouts for nearly a quarter of it's workforce.
AI adoption for businesses is a 24-month journey in a perfect scenario. They aren't seeing adoption fast enough to offset their CapEx spending... and they aren't alone. Add Anthropic's measly revenue numbers (compared to their CapEx), and hopefully you get the point.
These aren't buyouts because Microsoft found efficiencies with AI, they are buyouts because they (the AI industry) spend trillions (collectively) building the best tools the world has ever seen, and no one knows how to use them. digitalmeld.io/blog/who-is-p…
BREAKING: Microsoft, $MSFT, is offering buyouts to 60,000 workers as part of the company’s efforts to cut costs amid AI adoption, per Bloomberg.
Personnel on select teams in the US, Canada, India, and the UK at a vice president level and below will be eligible.
This reflects ~27% of Microsoft's total workforce.
