Here is an exclusive open release launch update from Marktechpost team. You can access 'AI2025Dev' without even any signup or login requirements…
Marktechpost Releases 'AI2025Dev': A Structured Intelligence Layer for AI Models, Benchmarks, and Ecosystem Signals
Marktechpost has rolled out the latest release of ai2025.dev, a 2025-focused analytics platform that turns model launches, benchmarks, and ecosystem activity into a structured dataset you can filter, compare, and export. Marktechpost is a California-based AI news platform, and this release extends its coverage from reporting into measurable tracking across models, papers, people, and capital.
What the platform now covers (2025 scope)
1) AI Releases in 2025 (AI insights layer) ai2025.dev’s release layer aggregates 100+ major releases across 39+ active companies, with explicit tagging for Open Source / Open Weights / Proprietary and a “flagship” marker for frontier-tier launches. The dashboard surfaces year-level indicators such as total releases, open-share, active vendors, and flagship count, making it easier to reason about release cadence and licensing posture without manually reconciling model cards and announcements.
2) Key Findings 2025 (trend extraction from the dataset) The platform highlights 3 recurring shifts visible in 2025 metadata: (a) Open weights expansion as a default distribution strategy for competitive models (b) Growth in agentic/tool-using model classes (systems optimized for tool execution rather than passive chat) (c) Efficiency gains via distillation and related compression recipes that shift capability into smaller footprints.
(3) LLM Training Data Scale in 2025 (tokens + timeline) A dedicated view tracks training token scale from 1.4T to 36T and aligns each point with a release date. This adds a practical axis to compare releases: not just “what shipped,” but the implied training budget behind it.
(4) Performance Benchmarks + Leaderboards (evaluation layer) ai2025.dev includes an Intelligence Index computed from standard benchmarks such as MMLU, HumanEval, and GSM8K, plus sortable leaderboards and a side-by-side comparison workflow. These benchmarks remain widely used but are increasingly saturated at the frontier, so the ability to inspect per-benchmark deltas (not only a single composite score) matters when models cluster near the top.
What’s new beyond models: ‘Top 100’ ecosystem indexes
This release also expands into ecosystem mapping with dedicated sections for: Top 100 research papers, Top 100 AI researchers, Top AI startups, Top AI founders, Top AI investors and funding views……. Check out the analytics platform here
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