#continuallearning
Live, measured metrics for the hashtag #continuallearning from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #continuallearning
This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.
Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-11-11 05:11 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · fosstodon.org (Mastodon public search API) · fetched 2026-11-11 05:11 UTCNo related tags with measured usage found for #continuallearning.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-11-11 05:11 UTCEverything below is measured over the latest 40 public posts (spanning ~34517 hours).
Posting hours (UTC) — busiest: 09:00
Languages: English (37) · Korean (1) · French (1) · German (1)
Avg boosts / post: 0.5
Top of the latest posts
Another day another launch 😬 I am super excited to announce that we open sourced Renate, a #ContinualLearning library to automatically retrain and retune deep neural networks. #deeplearning #opensource #aws https://github.com/awslabs/Renat
Interesting Times에서 드와르케시 파텔은 현 LLM이 장기 기억과 세션 간 지속 학습이 부족해 매번 새 인턴처럼 시작하며, 인간의 컴퓨터 기반 인지 업무를 엔드투엔드로 수행하는 AGI와는 거리가 있다고 평가했다. 다만 이 격차가 해소되면 단순 검색 챗봇을 넘어 서버에서 경제적 가치를 창출하는 인간급 시스템이 등장해 고용과 경제를 크게 바꿀 수 있다고 전망했다. 새 기술 발표가 아닌 업계 관찰·예측 인터뷰다. htt
fly51fly (@fly51fly) MetaCircle 연구진이 리플레이(replay) 학습 중 발생하는 급격한 과적합 현상인 ‘Forking’을 다룬 논문을 공개했습니다. 지속 학습, 오프라인 RL, 경험 리플레이를 활용하는 후학습 파이프라인에서 데이터 재사용이 모델 일반화를 갑자기 무너뜨릴 수 있는 조건과 완화책을 검토할 만한 초기 연구입니다. https://x.com/fly51fly/status/2106134412299
#continuallearning across platforms
every network with a public tag surfaceFollow #continuallearning straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/continuallearning