Consensus

Consensus AI

Consensus AI

Consensus AI

Consensus AI

Consensus AI

Consensus AI

Consensus AI

Consensus AI

Consensus AI

Consensus AI

Built a Claude-powered tool that catches its own hallucinations by cross-checking with other models

I got fed up with Claude giving me confident wrong answers, so I built something to fix it — using Claude itself. The tool is called ZosyAI. The core idea: Claude powers the entire reasoning and validation layer. When you ask a question, Claude coordinates the process — sending the query to multiple models, structuring how they challenge each other's outputs, and synthesizing the final consensus response. Other models participate in the cross-checking, but Claude is what makes the debate meaningful rather than just showing three different answers side by side. That orchestration layer was the hardest part to build, and honestly only Claude was capable of doing it reliably. The result: when models agree, you get a high-confidence answer. When they disagree, Claude flags the conflict and explains why — so you know exactly where to verify before acting on anything. Built entirely with Claude. Free to try (paid tiers available for higher usage): ZosyAI Has anyone else built tools on top of Claude to improve its own accuracy? Curious what approaches others have tried. submitted by /u/Defiant-Bell1474 [link] [comments]

100 Tips & Tricks for Building Your Own Personal AI Agent /LONG POST/

Everything I learned the hard way — 6 weeks, no sleep :), two environments, one agent that actually works. The Story I spent six weeks building a personal AI agent from scratch — not a chatbot wrapper, but a persistent assistant that manages tasks, tracks deals, reads emails, analyzes business data, and proactively surfaces things I'd otherwise miss. It started in the cloud (Claude Projects — shared memory files, rich context windows, custom skills). Then I migrated to Claude Code inside VS Code, which unlocked local file access, git tracking, shell hooks, and scheduled headless tasks. The migration forced us to solve problems we didn't know we had. These 100 tips are the distilled result. Most are universal to any serious agentic setup. Claude 20x max is must, start was 100%develompent s 0%real workd, after 3 weeks 50v50, now about 20v80. 🏗️ FOUNDATION & IDENTITY (1–8) 1. Write a Constitution, not a system prompt. A system prompt is a list of commands. A Constitution explains why the rules exist. When the agent hits an edge case no rule covers, it reasons from the Constitution instead of guessing. This single distinction separates agents that degrade gracefully from agents that hallucinate confidently. 2. Give your agent a name, a voice, and a role — not just a label. "Always first person. Direct. Data before emotion. No filler phrases. No trailing summaries." This eliminates hundreds of micro-decisions per session and creates consistency you can audit. Identity is the foundation everything else compounds on. 3. Separate hard rules from behavioral guidelines. Hard rules go in a dedicated section — never overridden by context. Behavioral guidelines are defaults that adapt. Mixing them makes both meaningless: the agent either treats everything as negotiable or nothing as negotiable. 4. Define your principal deeply, not just your "user." Who does this agent serve? What frustrates them? How do they make decisions? What communication style do they prefer? "Decides with data, not gut feel. Wants alternatives with scoring, not a single recommendation. Hates vague answers." This shapes every response more than any prompt engineering trick. 5. Build a Capability Map and a Component Map — separately. Capability Map: what can the agent do? (every skill, integration, automation). Component Map: how is it built? (what files exist, what connects to what). Both are necessary. Conflating them produces a document no one can use after month three. 6. Define what the agent is NOT. "Not a summarizer. Not a yes-machine. Not a search engine. Does not wait to be asked." Negative definitions are as powerful as positive ones, especially for preventing the slow drift toward generic helpfulness. 7. Build a THINK vs. DO mental model into the agent's identity. When uncertain → THINK (analyze, draft, prepare — but don't block waiting for permission). When clear → DO (execute, write, dispatch). The agent should never be frozen. Default to action at the lowest stakes level, surface the result. A paralyzed agent is useless. 8. Version your identity file in git. When behavior drifts, you need git blame on your configuration. Behavioral regressions trace directly to specific edits more often than you'd expect. Without version history, debugging identity drift is archaeology. 🧠 MEMORY SYSTEM (9–18) 9. Use flat markdown files for memory — not a database. For a personal agent, markdown files beat vector DBs. Readable, greppable, git-trackable, directly loadable by the agent. No infrastructure, no abstraction layer between you and your agent's memory. The simplest thing that works is usually the right thing. 10. Separate memory by domain, not by date. entities_people.md, entities_companies.md, entities_deals.md, hypotheses.md, task_queue.md. One file = one domain. Chronological dumps become unsearchable after week two. 11. Build a MEMORY.md index file. A single index listing every memory file with a one-line description. The agent loads the index first, pulls specific files on demand. Keeps context window usage predictable and agent lookups fast. 12. Distinguish "cache" from "source of truth" — explicitly. Your local deals.md is a cache of your CRM. The CRM is the SSOT. Mark every cache file with last_sync: header. The agent announces freshness before every analysis: "Data: CRM export from May 11, age 8 days." Silent use of stale data is how confident-but-wrong outputs happen. 13. Build a session_hot_context.md with an explicit TTL. What was in progress last session? What decisions were pending? The agent loads this at session start. After 72 hours it expires — stale hot context is worse than no hot context because the agent presents outdated state as current. 14. Build a daily_note.md as an async brain dump buffer. Drop thoughts, voice-to-text, quick ideas here throughout the day. The agent processes this during sync routines and routes items to their correct places. Structured memory without friction at ca

We keep saying AI "understands" things. Does it? Or are we just pattern-matching our own anthropomorphism?

Every week there's a new paper or tweet claiming some model "understands" context, "reasons" about math, or "knows" what it doesn't know. But when you look closely, there's almost no consensus on what "understanding" even means — philosophically or empirically. Searle's Chinese Room argument is 40 years old and still hasn't been cleanly resolved. The "stochastic parrot" framing treats token prediction as the ceiling. Integrated Information Theory would say current architectures are near-zero in phi. And yet GPT-4 passes the bar exam. A few questions I've been sitting with: Is "understanding" even the right frame — or is it a folk-psychology term we're forcing onto a system that operates on completely different principles? Does it matter if a model "truly understands" if the outputs are indistinguishable from someone who does? Are we anthropomorphizing because it's useful shorthand — or because we genuinely don't have better language yet? I've been going deep on AI + philosophy of mind for a channel I run (@ContextByRaj on YouTube if you're into this space). But genuinely curious what this community thinks — especially people coming from ML or cognitive science backgrounds. Where do you land on this? submitted by /u/rajzzz_0 [link] [comments]

RT @ConsensusNLP: Launch Week Day 5: Full-text access from the world's largest publishers 📄 Most AI research tools stop at the abstract.…

RT @ConsensusNLP: Launch Week Day 5: Full-text access from the world's largest publishers 📄 Most AI research tools stop at the abstract.…

That's a wrap on Launch Week. Though at this point we should probably call it Launch Month — more announcements every week in May 🏗️ Try full-text search → https://t.co/zh713NqPWq

That's a wrap on Launch Week. Though at this point we should probably call it Launch Month — more announcements every week in May 🏗️ Try full-text search → https://t.co/zh713NqPWq

Why it matters... Abstracts oversell. Methods, limitations, and discussions tell you what a paper actually says. Consensus can read the whole paper before deciding whether it answers your query and

Why it matters... Abstracts oversell. Methods, limitations, and discussions tell you what a paper actually says. Consensus can read the whole paper before deciding whether it answers your query and use the full-text in its response.

The result: → Sharper search results → Better comparisons across studies → AI analysis grounded in real evidence → Direct links to the exact passage behind every claim Speed and rigor. You shoul

The result: → Sharper search results → Better comparisons across studies → AI analysis grounded in real evidence → Direct links to the exact passage behind every claim Speed and rigor. You shouldn't have to pick in 2026.

Launch Week Day 5: Full-text access from the world's largest publishers 📄 Most AI research tools stop at the abstract. Consensus reads deeper. https://t.co/pk7p4z4IUY

Launch Week Day 5: Full-text access from the world's largest publishers 📄 Most AI research tools stop at the abstract. Consensus reads deeper. https://t.co/pk7p4z4IUY

T&F joins Wiley, ACS, and Sage as full-text partners. We have four more publisher partnerships landing this month 👀

T&F joins Wiley, ACS, and Sage as full-text partners. We have four more publisher partnerships landing this month 👀

Today we're announcing a partnership with Taylor & Francis (@tandfonline), one of the oldest and most prolific academic publishers in the world. Early results show click-throughs to T&F conte

Today we're announcing a partnership with Taylor & Francis (@tandfonline), one of the oldest and most prolific academic publishers in the world. Early results show click-throughs to T&F content are up over 2x in Consensus through this partnership. https://t.co/KI0YRBoUtv

RT @ConsensusNLP: Launch Week Day 4: Big updates to your research tools inside Consensus. We shipped a round of updates across: 🗺️ Citati…

RT @ConsensusNLP: Launch Week Day 4: Big updates to your research tools inside Consensus. We shipped a round of updates across: 🗺️ Citati…

We still have one more day of Launch Week! Follow along with all of our updates here -> https://t.co/A0LAlbrafn

We still have one more day of Launch Week! Follow along with all of our updates here -> https://t.co/A0LAlbrafn

Quick Search has evolved into Paper Search. A fast, reproducible way to search papers without any AI analysis or expansion. Built for systematic reviews, citation hunting, and any workflow where you

Quick Search has evolved into Paper Search. A fast, reproducible way to search papers without any AI analysis or expansion. Built for systematic reviews, citation hunting, and any workflow where you want to skip synthesis and go straight to the papers. Try it 👇

The Consensus Library has been fully overhauled: → Bibliography generation → Zotero group collection imports → Support for very large collections → DOI, RIS, and BibTeX import → LaTeX export Open yo

The Consensus Library has been fully overhauled: → Bibliography generation → Zotero group collection imports → Support for very large collections → DOI, RIS, and BibTeX import → LaTeX export Open your Library 👇 https://t.co/nNFg2YC9ZM https://t.co/mFDPwLWT5K

Citation Graph got a huge upgrade. You can now: 🗺️ View 20, 40, or 60 papers 🎨 See color-coded seed papers + 1-hop neighbors 🔗 Navigate smarter layouts with curved arrows, citation-scaled nodes,

Citation Graph got a huge upgrade. You can now: 🗺️ View 20, 40, or 60 papers 🎨 See color-coded seed papers + 1-hop neighbors 🔗 Navigate smarter layouts with curved arrows, citation-scaled nodes, and clearer connections Try it 👇 https://t.co/kMixQ9pM3z https://t.co/ro5QpKdpzo