AI · alternatives · 2026
24 alternatives to Recall Predict
Ungameable, community-powered AI benchmarks
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
- 1PG
I’m Andrew, co-founder of Recall. Over the past few days I’ve been building Predict, a playground where anyone can: - propose skills we should measure in language models—live examples include difficult math, memory-manipulation resistance, code generation, and empathy under bad news - write evals (graded prompts) for those skills - forecast which models will score highest once GPT-5 is released Why this exists Benchmarks leak into training data quickly; scores are unreliable and labs still declare progress. The prediction tool aims keeps the target moving by letting the crowd define both the…
2025 · its alternatives →
- 2
Active Recall▲1,078Chat with everything you’ve read, heard, watched, or noted
2024 · recall.it · its alternatives →
- 3
Stop wasting tokens and re-explaining your project every session. Recall gives Claude Code durable memory — entirely offline. - raiyanyahya/recall
Jun 2026 · github.com · its alternatives →
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- 5IV
The local Artificial memory for your device.
2024 · recallmemory.io · its alternatives →
- 6UD
Hey HN! I’m the founder of Unify, and we’ve just released our Model Hub, which provides a collection of LLM endpoints with live runtime benchmarks all plotted across time: https://unify.ai/hub A key finding is that static tabular runtime benchmarks for LLMs simply do not work. It’s necessary to take a time-series perspective, and plot the variations through time. We currently have 21 models provided by: Anyscale, Perplexity AI, Replicate, Together AI, OctoAI, Mistral AI and OpenAI, with more on the roadmap. We test across different regions (Asia, US, Europe), with varied…
2024 · its alternatives →
- 7
APIEval-20▲121An open benchmark for AI agents that test APIs
May 2026 · resources.kusho.ai · its alternatives →
- 8
We built a computer use agent once and got it benchmarked at the 2nd position in the biggest benchmark for this field but in the process, we realized one thing, how outdated and inefficient these benchmarks are and how they don't truly capture how these tasks went. So we thought a community led benchmarking arena would be the way to go about. https://coarena.ai/. You just have to put in a task and two side by side windows with hidden models will show up and do the same task and at the end you vote which one did better(or none). We have a leaderboard where we rank all the…
Aug 2026 · coarena.ai · its alternatives →
- 9
Papr▲113Predictive memory and context intelligence API for AI Agents
Dec 2025 · papr.ai · its alternatives →
- 10
DeltaMemory▲104Most AI agents forget everything between sessions. We built DeltaMemory to fix that. It's a cognitive memory layer that gives agents persistent recall, automatic fact extraction, and temporal reasoning — via a single SDK call. Key numbers: - 89% accuracy on LoCoMo long-term conversation benchmark - 50ms p50 retrieval latency - 97% cost reduction vs raw token re-processing Open source SDKs, works with any LLM stack. Currently in early access. Happy to answer questions about the architecture, benchmark methodology, or how we handle knowledge graphs.
Feb 2026 · deltamemory.com · its alternatives →
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I've been working on this tool that lets you build a personal knowledge graph from articles, blog posts, podcasts, YouTube videos, and other content you find interesting online. You can safely forget everything and trust that Recall will resurface it when something new that is related comes up. Looking forward to your thoughts and feedback on how it could be improved! The original version of Recall was posted last year nov on HN: https://news.ycombinator.com/item?id=33425947 Since then I have pivoted to a browser extension.
2023 · recall.wiki · its alternatives →
- 13
Let’s see who can predict the future.
Feb 2026 · predictionary.com · its alternatives →
- 14
- 15ΤB
τ-Bench is an open benchmark for evaluating AI agents on grounded, multi-turn customer service tasks with verifiable outcomes. It's been great to see the community adopt it since launch — this is now the third iteration. With τ³-Bench, we're extending it to two new settings: knowledge-intensive retrieval and full-duplex voice. τ-Knowledge: agents must navigate ~700 interconnected policy documents to complete multi-step tasks. Best frontier model (GPT-5.2, high reasoning) hits ~25%. The surprising part: even when you hand the model the exact documents it needs, performance only reaches ~40%.…
Mar 2026 · its alternatives →
- 16
We’re excited to share ML-Dev-Bench, a new open-source benchmark that tests AI agents on real-world ML development tasks. Unlike typical coding challenges or Kaggle-style competitions, our benchmark simulates end-to-end ML workflows including: - Dataset handling and preprocessing - Debugging model and code failures - Implementing new model architectures - Fine-tuning and improving existing models With 30 diverse tasks, ML-Dev-Bench evaluates agents across critical stages of ML development. To complement this, we built Calipers, a framework that provides systematic performance evaluation and…
2025 · github.com · its alternatives →
- 17
Cribble.dev▲14Gamify AI usage. Climb rankings. Attack and Rule the board.
2025 · cribble.dev · its alternatives →
- 18AL
Hi HN! We partnered with the Atlas team to build a tool called AI Predict [0] that allows anyone to ask any question about the future and get a thoroughly researched, AI-generated prediction on how likely it is to be true. How it works: Atlas replicated a Berkeley paper [1] that showed LLMs could make predictions as accurate as the crowd. We’re using a mix of models from OpenAI and Anthropic, with information retrieval powered by NewsCatcher [2]. The system is live and fully functional, though it might struggle with hyper-local questions outside of the public domain (e.g., “Will I have…
2024 · aipredict.fun · its alternatives →
- 19
Recall▲16One developer solves it. Every developer knows it.
Feb 2026 · recall.team · its alternatives →
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- 21
I built a small benchmark to test CLI coding agents on blind bug detection. A challenger agent injects bugs and writes ground truth (`bugs.json`). A different reviewer agent audits the repo without seeing ground truth, and an LLM matcher scores bug-to-finding assignments. Current run: 50 repos, 150 challenges, 450 reviews, 2,603 injected bugs. Weighted detection: Claude 58.05%, Codex 37.84%, Gemini 27.81%. LLM-judge benchmarks are easy to get wrong, so I’d really appreciate critical feedback on benchmark fairness, scoring/matching methodology, and obvious failure modes I’m missing. Full…
Feb 2026 · github.com · its alternatives →
- 22
Track how AI models feel in everyday use through public community feedback, 7-day experience scores and trends. This is not a capability benchmark.
Aug 2026 · isaidumber.today · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →