The roadmap remains firmly on track, the team has never stopped building. Major upgrades to the Points System and Harvester System are rolling out very soon! TGE is 100% locked in to complete within the next 2 months, kicking off a sustainable global economic loop and exciting new gameplay explorations. Stay tuned. Time will prove everything!

RobinPerps Roadmap is taking shape. Phase 01: Foundation Website launch, wallet connect, early community. Phase 02: Airdrop Daily check-ins, tasks, badges, and leaderboard. Phase 03: Earn Vault ETH deposits, daily points, user dashboard. Phase 04: Perps UI Market views, position tracking, trading workflow prep. Phase 05: Growth Referrals, campaigns, API volume leaderboard. Phase 06: Launch+ More markets, more rewards, ecosystem expansion. Community → Points → Earn → Perps UI → Growth → Full ecosystem.

Delivering on our Whitepaper & Roadmap: First $RT Buyback & Burn Incoming!🔥 In line with our long-term commitment, 20% of all ecosystem platform revenue is permanently allocated to continuous $RT buybacks and burns. Upcoming Burn Details: Total Buyback Value: 268,200 USDT Estimated Tokens to Burn: 4,470,000 $RT The transaction hash will be published immediately upon completion for full on-chain transparency. Keep building!

I just published my roadmap for building evals on highly subjective tasks. Here are the key steps involved and practical tricks for reducing noise / increasing confidence… Blog is here: Why is this difficult? Evaluating agents is already hard due to their broad scope, emphasis on expensive / long-horizon tasks, and non-determinism. Subjective tasks make this even harder because we cannot deterministically verify solutions and must rely on human judgments (or LLM judges). This introduces additional noise into an already difficult evaluation process. Evaluation roadmap. We can build reliable evaluations using a repeatable framework with three components: - Human evaluation: Our North Star for quality. Create quality guidelines that clearly define quality with the help of subject-matter experts, calibrate human evaluators to ensure agreement, and continually monitor evaluation quality. - Offline evaluation: Use calibrated LLM judges or task-specific rubrics as an efficient proxy for human evaluation. This allows us to scale experimentation without relying on expensive human evaluation for every new model. - Production monitoring: Track quality signals from real-world agent usage and use A/B testing to validate improvements before fully deploying a new agent. Multi-stage evaluation. These components form a tiered evaluation process: Offline evaluation → Human evaluation → Production A/B test → Full deployment We can test many ideas automatically, send promising candidates to human evaluation, and validate improvements through production A/B testing. Continual production monitoring then allows us to discover new use cases / failure cases in production that we can add into the earlier stages of evaluation to create a feedback loop. Sampling useful data. Evaluation quality depends heavily on which tasks we select. We want tasks that are diverse, realistic, difficult, discriminative, and accurate. Techniques like structured task taxonomies, model-based difficulty filtering, Item Response Theory (IRT), and LLM-based auditing help us construct more informative evaluation sets. Handling noise. We should leverage statistics throughout the evaluation process: - Power analysis: Estimate the number of evaluation samples needed to detect meaningful improvements. - Confidence intervals: Quantify uncertainty and provide clear confidence signals alongside evaluation results. - Re-sampling: Repeat agent / judge evaluations to reduce within-task variance and potentially tighten confidence intervals. - Paired comparisons: Evaluate models on the same tasks to more reliably detect performance differences. Key takeaway. An improved benchmark score does not always mean our agent is more capable. We need well-designed evaluation tasks, calibrated human / model evaluators, and statistical confidence signals to distinguish meaningful capability improvements from noise (especially on subjective tasks). By combining these techniques into a repeatable evaluation process / roadmap, we can make building and maintaining evaluation suites more systematic, reliable, and scalable.

Polygon's Gigagas roadmap targets 100,000 TPS. By mid-2026 the PoS chain hit about 5,000 TPS of peak capacity. Visa added @0xPolygon to its stablecoin settlement program in April, making the $POL network one rail among several. Gigagas so far: