Alternatives
Products that do what MARK C — Counterfactual Intelligence does
MARK C — See what happens before you decide.
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Jan 2026 · extremeclarity.ai
- 20IB
Hi HN, I'm the creator of this project. For the past months, I've been working on building an AI agent that could move beyond simple generation and tackle inventive challenges autonomously. The core idea was to create a system with a "metacognitive loop"—the ability to recognize when it's stuck on a fundamental problem and then launch a sub-mission to solve that specific bottleneck before continuing. The linked article is a deeper introduction to the system's architecture and a snapshot from a recent run. I tried to design it to be evidence-grounded and self-critical to avoid the pitfalls of…
2025 · robw1se.substack.com
- 21AH
This paper formally defines where current AGI hits a structural wall — not a technical one. It shows that no amount of scaling, reinforcement learning, or recursive optimization will break through three deep epistemological and formal constraints: 1. Semantic Closure — An AI system cannot generate outputs that require meaning beyond its internal frame. 2. Non-Computability of Frame Innovation — New cognitive structures cannot be computed from within an existing one. 3. Statistical Breakdown in Open Worlds — Probabilistic inference collapses in environments with heavy-tailed uncertainty.…
2025
- 22IM
Atrophy is an iOS self-report quiz aimed at software engineers who use LLMs heavily enough at work to wonder if they're trending toward AI over-reliance or some form of AI psychosis. I built it because I noticed a pattern: formerly AI-skeptical coworkers now open every standup or design discussion with "I asked Claude..." or "Claude told me..." for technical problems and design decisions. I've felt the same pull myself to delegate every task or problem to AI. It's easy to lean on these tools for almost any amount of critical thinking or problem solving, and I'm worried about what it means…
May 2026 · apps.apple.com
- 23MC
Hi HN, I’ve been building AI agents and copilots, and kept running into a frustrating problem: they don’t fail loudly, they forget things quietly. Users re-explain preferences, agents contradict earlier responses, and context resets without any clear visibility into why. I built Memograph CLI as a debugging tool to analyze conversation transcripts and show: - what the agent forgot - where continuity broke - contradictions and repeated context - estimated token waste due to re-prompting It works locally and supports plain text or JSON transcripts. Example: $ memograph Output: Cognitive Drift…
Feb 2026
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