ok you HAVE to see this…
a guy in Minnesota bought the giant X off the old Xcel Energy Center (now Grand Casino Arena) and rigged it to flash in his yard every time the Wild score.
live stream 👇
lightthex.com/
Wtf
Strategy has acquired 34,164 BTC for ~$2.54 billion at ~$74,395 per bitcoin and has achieved BTC Yield of 9.5% YTD 2026. As of 4/19/2026, we hodl 815,061 $BTC acquired for ~$61.56 billion at ~$75,527 per bitcoin. $MSTR $STRC strategy.com/press/strategy-…
Redbeard retweeted
Don’t forget to effortmaxx your models so that you can tokenmaxx your work and expensemaxx your OpEx.
Have a good week.
Redbeard retweeted
Best money I've ever spent as a CEO... an internal AI transformation hire.
He doesn't care about title. He just wants to ship. And he goes across your entire org, sales, revenue, hr, apps, tech and kills stupid manual processes.
Such an underrated unlock I have since hired 2 more.
Redbeard retweeted
TODAY AT 11AM ET: President Trump will be giving remarks on TRUMP ACCOUNTS, one of the greatest feats from the One Big Beautiful Bill 💸👶
Learn more about how to sign your child up for a Trump Account here: trumpaccounts.gov/
Redbeard retweeted
The entire thesis for $MSTR is that they are betting on the CAGR of Bitcoin being higher than their cost of capital which is ~11%.
They are executing a carry trade on Bitcoin.
If you're bullish on Bitcoin, you should be bullish on Strategy.
It really is that simple.
This
“custom finetuning is dead. Stop wasting time on it. Frontier is moving too fast to invest 8 weeks into finetuning. Costs are dropping too quickly for price to matter. Better prompting will take you very far and this will only become more true as instruction following improves”
yes things are changing fast, but also I see companies (even faang) way behind the frontier for no reason.
you are guaranteed to lose if you fall behind.
the no unforced-errors ai leader playbook:
For your team:
- use coding agents. give all engineers their pick of harnesses, models, background agents: Claude code, Cursor, Devin, with closed/open models. Hearing Meta engineers are forced to use Llama 4. Opus 4.5 is the baseline now.
- give your agents tools to ALL dev tooling: Linear, GitHub, Datadog, Sentry, any Internal tooling. If agents are being held back because of lack of context that’s your fault.
- invest in your codebase specific agent docs. stop saying “doesn’t do X well”. If that’s an issue, try better prompting, agents.md, linting, and code rules. Tell it how you want things. Every manual edit you make is an opportunity for agent.md improvement
- invest in robust background agent infra - get a full development stack working on VM/sandboxes. yes it’s hard to set up but it will be worth it, your engineers can run multiple in parallel. Code review will be the bottleneck soon.
- figure out security issues. stop being risk averse and do what is needed to unblock access to tools.
in your product:
- always use the latest generation models in your features (move things off of last gen models asap, unless robust evals indicate otherwise). Requires changes every 1-2 weeks - eg: GitHub copilot mobile still offers code review with gpt 4.1 and Sonnet 3.5 @jaredpalmer. You are leaving money on the table by being on Sonnet 4, or gpt 4o
- Use embedding semantic search instead of fuzzy search. Any general embedding model will do better than Levenshtein / fuzzy heuristics.
- leave no form unfilled. use structured outputs and whatever context you have on the user to do a best-effort pre-fill
- allow unstructured inputs on all product surfaces - must accept freeform text and documents. Forms are dead.
- custom finetuning is dead. Stop wasting time on it. Frontier is moving too fast to invest 8 weeks into finetuning. Costs are dropping too quickly for price to matter. Better prompting will take you very far and this will only become more true as instruction following improves
- build evals to make quick model-upgrade decisions. they don’t need to be perfect but at least need to allow you to compare models relative to each other. most decisions become clear on a Pareto cost vs benchmark perf plot
- encourage all engineers to build with ai: build primitives to call models from all code bases / models: structured output, semantic similarity endpoints, sandbox code execution. etc
What else am I missing?
Redbeard retweeted
Mike Newcome plans to address voter discontent with a centrist approach in his run for governor, incorporating ideas from both Republicans, Democrats and others. kstp.com/kstp-news/top-news/…
Redbeard retweeted
The trick is the workflow, not the model. If you drop AI into a wide open task and expect magic, it will disappoint you every time. You have to design narrow steps where the AI only makes one or two decisions at a time and you refine those steps over and over.
Once you build a system like that the usefulness goes through the roof. That’s why it works for us and why most people think AI is overrated…They are giving it a blank canvas and I am giving it a conveyor belt.