Finn is described as an AI customer service agent that can automatically resolve up to 93% of customer queries.
Wix has not yet cracked TikTok and sees large upside in TikTok and LinkedIn despite imperfect targeting on LinkedIn and past disappointment with sports endorsements.
Omer Shai rejects LTV as a primary marketing metric because it is unknowable and too slow for fast decisions, and prefers TROI (time to return on investment).
Cta Dispersion Drivers Speed Universe And Volatility Targeting
CTA return dispersion was described as being explainable by implementation choices including trading speed, market set, and volatility-adjusted position sizing, even when return correlations are high.
It was asserted that 2025 trend performance was driven by a very narrow band of assets, while early 2026 shows broader trends across metals and other commodities.
Central banks have been net buyers of gold every year since 2011, after being net sellers each year from 2000 to 2009.
Version Lineage And Preservation Targets
Crimsonland’s release lineage includes a 2002 freeware prototype series, a 2003 shareware v1.8–v1.9 line, and a GOG “classic” build v1.9.93 (Feb 2011) that was later bundled as a bonus alongside the 2014 remaster.
A Ghidra-driven workflow maintains a name_map.json to iteratively rename and type functions based on evidence such as strings, call patterns, and struct sizes, allowing improved types to propagate across decompilations.
Crimsonland assets are stored in custom PAQ archives with magic 'paq\0' and entries consisting of filename, size, and payload, using Windows-style backslash paths.
Lower confidence
Collective Efficacy Framing
Matt Webb published a piece arguing that people can "just do things" to improve their communities.
A community can bootstrap a shared public-good project by organizing collectively, producing needed infrastructure, securing subsidies, and redistributing costs to include lower-income participants.
Small collective projects can escalate into sustained political engagement, including contacting representatives and tracking legislation to embed the change into building requirements.
Training Cost/Time Baselines For Gpt-2-Level Capability
In 2019, GPT-2 training reportedly used 32 TPU v3 chips for 168 hours at about $8 per TPUv3-hour, totaling roughly $43,000.
A roughly 600× reduction over seven years (about 2.5× per year) in the cost to train a GPT-2-level model is claimed based on comparing the GPT-2 cost baseline to the nanochat cost estimate.
With recent improvements merged into nanochat (many originating from modded-nanogpt), a higher CORE score than GPT-2 can reportedly be reached in about 3.04 hours for roughly $73 on a single 8xH100 node.