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$EFFECT AI Alpha Call Recap – June 27, 2025. Author: @MiggyCrypt .Current Metrics and Milestones:.- Tasks Completed: 42,000+.- Task Types Introduced: 6.- Active Testers: ~40 total across both Alpha phases. Top Contributors:.- BashorunDin0: 17,000+ tasks.- Nadiadanova, Fandngo,.

RT @effectaix: Two Alpha phases down. Big things ahead. Let’s catch up 👇.We just published a full Alpha recap: what we tested, what we lear….

RT @effectaix: 1/7 🧵.We’re incredibly pleased with how the second wave of Effect Alpha testing played out. The energy, feedback, and hustle….

Effect AI Alpha Call Recap May 23rd, 2025.$EFFECT .Author: @MiggyCrypt .Current Metrics:.- Tasks Completed: 5,200+.- Active Workers: ~15–20.- Task Types Released: 3 so far. Key Takeaways from Initial Testing:.- Testing has been highly successful. - Early testers have provided.

Recording:.

May 7th, 2025 Effect AI Community Call Summary.Author: Miguel | Effect AI. Platform Progress & Internal Testing.Near-Launch Readiness:.Internal testing continues to show solid progress, with recent bug fixes and UI improvements implemented. The team has been actively refining.

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Another clip! $EFFECT Roadmap will follow the initial alpha currently being tested for the new Solana based microtasking system.

@djstrikanova @nosana_ai @effectaix The ultimate goal: Build a living, public library of proven model + configuration pairings with quantified performance gains. This moves open-source AI optimization from anecdote to data-driven science, unlocking potential & democratizing high-performance AI.

@djstrikanova @nosana_ai @effectaix This demands robust infrastructure. The @nosana_ai network (for decentralized AI compute & eval) + @effectaix workforce (for scaled human insight) create a powerful synergy on Solana to enable rapid, cost-effective hybrid benchmarking & calibration via "Meta-Benchmarks".

@djstrikanova @nosana_ai @effectaix Finding the best configurations requires "Systematic Configuration Mining." Validating them needs more than simple scores; a Hybrid Evaluation approach using AI scale + human depth is essential to assess quality, safety, nuance & real-world usefulness accurately.

@djstrikanova @nosana_ai @effectaix Current AI benchmarks measure baseline model power, but crucially miss how performance changes with optimized configurations. We need "Prompt-Plus Benchmarking" – testing models paired with specific configurations against standard tests to see their true potential.

@djstrikanova @nosana_ai @effectaix Why do open-source LLMs often seem less polished than proprietary ones? A key reason is the lack of optimized 'Prompting Configurations' (system prompts + interaction strategies) beyond defaults. This creates an "Optimization Gap".

AI benchmarks show baseline power, but miss the gains from smart prompting ('Prompting Configurations'). @djstrikanova outlines a vision for "Prompt-Plus Benchmarking" using @nosana_ai & @effectaix to measure AI, revealing true capabilities. $NOS $EFFECT.

@JesseEisses