Checkmate(CHECK) Регулярные инвестиции

The article "The Midlife Crisis of Crypto GPs: No PMF, No LP's Next Check" analyzes the shifting crypto fundraising landscape. It argues that the era of LPs funding vague "vision" is over; GPs must now offer products with clear Product-Market Fit (PMF) to secure capital. The market has matured. LPs, disillusioned by the last cycle's failures and wary of long lock-up periods, now demand tangible, near-term returns rather than speculative narratives. The proliferation of accessible crypto ETFs and other liquid products has reduced the need for VC blind pools as an entry point. The author categorizes crypto fundraising products into three types: Primary (VC funds, with blind pools or clear pipelines), Liquid (alpha/beta, directional/market-neutral strategies), and CeFi/DeFi Native Yield (crypto-specific mechanisms like staking, farming). Focusing on the Primary market, the piece details why traditional LP rationales for investing in crypto VCs have weakened: easier beta access via ETFs, diminished "access" and "judgement" premiums as LPs build internal teams, and a widespread lack of proven superior returns from GPs. Ultimately, only specific players are likely to remain at the primary VC table: large funds with access to patient endowment capital, family offices/HNWIs investing proprietary capital, the few funds with demonstrable excess returns from the last cycle, and those with clear "deal-making" or ecosystem resource advantages. For others, the path forward is to rebuild trust by proving alpha-generation capability in a niche or providing concrete, valuable services.

The article "The Midlife Crisis of Crypto GPs: No PMF, No Next LP Check" analyzes the shifting crypto fundraising landscape. It argues the era of selling grand visions to LPs is over; GPs must now offer products with clear Product-Market Fit (PMF). The author categorizes crypto fundraising products into three types: Primary (VC funds), Liquid (trading strategies), and CeFi/DeFi Native Yield. This summary focuses on the Primary market. Key points include: * **Market Shift:** LPs are impatient, demand immediate returns, and are skeptical of future promises. The "easy money" narrative has faded. * **GP Value Erosion:** LP learning curves have shortened (aided by AI), reducing the value of a GP's basic "crypto knowledge." Superior judgment is now rare. * **Weakened LP Motivations:** Traditional reasons for LPs to invest in crypto VC funds (capturing industry beta, gaining access, leveraging GP judgment) have weakened due to new products like ETFs and increased LP sophistication. * **Surviving in Primary:** The primary market will likely persist for: 1) large funds in endowment mandates treating it as a lottery ticket, 2) family offices/HNWIs using proprietary capital, 3) a few funds with proven recent outperformance, and 4) funds with strong ecosystem "deal-making" capabilities. * **Conclusion:** For most GPs, rebuilding trust requires starting over in a niche, demonstrating alpha-generating ability, or providing concrete value/services to LPs.

Crypto trading volumes have been declining since July 2024, hitting their lowest two-year average, indicating weak demand and low market confidence. This thin liquidity can lead to sharp price moves. Against this backdrop, analyst Ali Martinez notes that Solana (SOL) has flashed a bullish buy signal on the 3-day chart using the SuperTrend indicator, with key levels to watch at $96 and $121. Furthermore, long-term holder data shows consistent accumulation throughout 2026. However, SOL's broader price structure remains bearish, having broken previous swing lows. Key resistance levels from Fibonacci analysis are at $83.79 and $90.22, with the $116 realized price level posing a major overhead supply hurdle. Recent large on-chain token movements, such as a $15.14 million transfer from Alameda Research, have also contributed to selling pressure and liquidations. In summary, while recent signals and holder activity offer a bullish near-term case, significant resistance levels and weak market-wide capital flows continue to challenge a sustained SOL recovery.

Bitcoin faces mixed signals as it tests $64,500. While traditional investors added modest inflows, long-term holders (those holding coins for 155+ days) remain a focal point of caution. On-chain data shows this group is still selling at a loss, with their Spent Output Profit Ratio (SOPR) at 0.94, indicating 6% losses—a significant improvement from earlier 27% losses but not yet a sign of full conviction. Market activity is currently dominated by short-term holders, as indicated by the Exchange Coin Days Destroyed (CDD) metric, suggesting a more relaxed selling phase. Exchange reserves have also declined slightly. However, macroeconomic risks pose a headwind. Rising oil prices, driven by Middle East tensions, threaten to reignite inflation concerns. This could dampen the risk-on sentiment crucial for Bitcoin and potentially lead US investors to reduce exposure via spot Bitcoin ETFs. The market's recovery hinges on balancing these improving on-chain dynamics against persistent external economic pressures.

Claude No Longer Submits Code Directly: 4 Self-Check Skills to Run Before Coming Back to You AI already writes code, but the burden of reviewing it still falls on you. To address this, Anthropic has built a "verification loop" into Claude Code. After writing code, Claude now runs four self-check skills before delivering the work: * `/code-review`: Finds potential bugs and provides review feedback. * `/simplify`: Cleans up the diff, removing redundant or over-complex implementations to reduce future maintenance costs. * `/verify`: Performs end-to-end validation, actually running the application to confirm the feature works, not just appears to. * `/design`: Used only for UI changes; cross-references the implementation against the project's DESIGN.md file. This loop extends the AI agent's workflow from "gather context → execute" to "gather context → execute → auto-verify → fix → re-verify." It tackles the new bottleneck in AI-assisted development: the speed of verifying code now outpaces human review. These skills are built on Claude Code's existing verification foundation (like running apps and using linters). Teams can create their own custom verification skills by documenting their repetitive manual checks in plain language as Markdown files. Verification can be triggered at four levels: manually (Standalone), embedded in a task, chained with other skills, or automatically on every PR (On every PR). The shift signifies that competition in AI programming is moving from code generation to robust verification and self-correction. Well-built verification loops allow AI agents to run longer and more autonomously with less human supervision. Skills, which encapsulate team knowledge and workflows, are becoming a cross-vendor standard, meaning a team's efficiency gap will depend less on the AI model and more on their investment in these automated workflows and verification mechanisms.