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On August 15, the AI community has been buzzing about the possibility of multiple versions of DeepSeek-V4-Pro. Some users discovered that when calling the deepseek-v4-pro API, changing the IP or recreating a session resulted in three different 'inference styles': one frequently starting with 'Let me', resembling the previous V4 Pro Preview; another often stating 'The user wants me', similar to V4 Flash; and a third heavily using 'we', referred to by some users as the more powerful 'God Version V4 Pro'. Since the performance tends to remain stable once a session enters a certain mode, the community speculated whether DeepSeek was hiding multiple models behind the API and distributing them through a routing mechanism. However, further analysis of the DeepSeek Harness source code provided another explanation: the differences may not stem from different model weights but rather from the Agent operating environment. The community noted that on August 10, the official DeepSeek Harness repository updated a key commit: 'fix(preset): align minimal agent with RL composition'. This update aimed to ensure that the Minimal Agent was consistent with the Agent environment used during reinforcement learning (RL) training. The official documentation indicates that the Minimal preset includes a minimal system prompt, a persistent Bash environment, specified editing tools, and a compaction policy used in RL training, while removing additional identity prompts, web prompts, and tool descriptions. This suggests that DSH Minimal may not be a 'stripped-down version' of the Standard version but rather simulates the real Agent environment encountered during model training. Community tests also support this view. The same DeepSeek V4 Pro performed differently across various Harness environments: DSH Standard: 91 points; DSH PTC: 92 points; DSH Minimal: 99/96 points. Subsequently, testers developed the 'Anchored Standard' plugin: the first request simulated the Minimal environment, only opening shell and read tools, and after completing the first tool call, restored the full Standard toolset, resulting in consecutive scores of 98/99 points. Testers believe that the key to the V4 Pro Agent's performance may not depend on how many tools it ultimately has, but rather on what the model first encounters: System Prompt + Tool Schema + Agent Scaffold. Therefore, the so-called 'three DeepSeek models' may actually be a combination of two layers of factors: on one hand, differences in API service environments, deployment configurations, or gray instances; on the other hand, whether the model enters an Agent environment close to the RL training distribution. However, this assertion has not yet been confirmed by DeepSeek officials. The official API documentation indicates that deepseek-v4-pro corresponds to the DeepSeek-V4-Pro-0813 official version and does not disclose a multi-model automatic routing mechanism. Currently, it appears that the different performances of DeepSeek-V4-Pro are more likely the result of the interplay between model weights, inference environments, and Agent frameworks, rather than the simple existence of three hidden models.

On August 15, according to Poloniex market data, PUMP has increased by 19.65% in the past 7 days and by 66.57% in the past 30 days, with its market cap now reaching $1.665 billion. This morning, the Pump.fun team and investors completed the monthly unlocking of tokens, releasing a total of 4.94 billion PUMP tokens, valued at approximately $13.6 million. The unlocked tokens were subsequently distributed to 125 wallet addresses.

On August 15, investors are re-betting on the US stock market as the panic from the tech stock plunge in July quickly dissipates, returning the market to a 'buy the dip' mode. Data shows that the S&P 500 index has risen about 4% since August, reaching a historical high this week; the Nasdaq 100 index has also rebounded strongly from the brief adjustment in July, currently just about 2.5% away from its June peak. Strong Q2 earnings reports, cooling inflation, and rising expectations for interest rate cuts have become the main driving forces behind the market rebound. Data indicates that demand for US information technology stocks has risen to its highest level in nearly five years, with institutional investors also increasing their bets on derivatives linked to stock index gains. Michael Metcalfe, head of macro strategy at State Street Global Advisors, stated that tech stock trading 'currently looks unassailable,' with strong earnings performance reinforcing the market's judgment that AI investments represent a long-term structural trend. This week, the decline in US inflation data has led the market to reduce bets on continued interest rate hikes, pushing the S&P 500 to break above the 7800-point mark. Meanwhile, oil prices have fallen from last month's high of $100 to about $88, leading to decreased investor concern over risks in the Middle East. Institutions are also raising their targets for US stocks: Citigroup has raised its S&P 500 target for the end of 2026 to 8100 points; JPMorgan Chase has increased its S&P 500 target from 7800 to 8000 points. The Q2 earnings season further boosts market confidence, with S&P 500 constituent companies seeing overall earnings grow by more than 50% year-on-year. Even excluding investment gains from Amazon and Google's parent company Alphabet, the earnings growth rate remains about 30%. AI-related sectors have once again become the core of the rebound. Since August, Super Micro Computer has risen about 37%, SanDisk has increased by about 33%, and cloud computing companies CoreWeave and Nebius Group have both seen gains exceeding 40%. However, the market's rapid shift to optimism has raised caution among some institutions. Deutsche Bank strategist Henry Allen noted that the market is currently pricing in a 'golden scenario': economic growth is maintained, central banks are only slightly tightening policies, Middle East supply shocks are easing, and oil prices are falling, but this combination 'leaves almost no room for error.' Currently, Wall Street is regaining upward momentum driven by the convergence of AI earnings realization, declining inflation, and rising risk appetite, but high valuations and geopolitical risks may still become sources of future market volatility.