DEV Community: IdeaSniper

Your agent passed 10,000 evals. It can still deadlock.

IdeaSniper — Wed, 28 Oct 2026 12:11:24 +0000

If your agent workflow has 20 routing variables — retry counters, confidence thresholds, tool-success flags, budget checks — the guards on its edges combine in 2^20 = 1,048,576 ways. Run 10,000 evals and you have sampled under 1% of them (0.95%, to be exact). The other 99% is where the stalls live.

A green eval suite proves the workflow can work. It says nothing about the combinations you didn't sample — including the ones where every outgoing edge of some node is closed at the same time and the agent has nowhere to go.

The failure mode

This is an illustrative example, but the shape is common. Take a research agent with a critic loop: a planner calls a tool executor, a critic node checks the result, and two edges leave the critic. One loops back for another attempt while retry_count < 3. The other exits — and whoever wrote it copy-pasted the same guard, so the exit also requires retry_count < 3.

Every node is correct in isolation. Every eval passes, because the evals never produce the state where the critic is reached with the counter already at 3. In production that state shows up on a Tuesday. Both edges are closed. The agent does not crash and does not error — it sits in the loop burning tokens and wall-clock time until something external kills it.

The bug is not in any node. It is in the combination of two guards, and combinations are exactly what sampled tests are worst at covering.

Tracing does not save you either. A tracing tool will show you the stalled run in detail — after it has burned the tokens, hit the timeout, and possibly retried against a paid API a few hundred times. Observability explains stalls that already happened. It cannot tell you whether a stall is possible in a graph you have not deployed yet. That check has to happen before the run, on the graph itself.

Why sampling can't rule this out

Evals sample paths in proportion to how likely your test data makes them. Trap states live in the corners your data rarely produces: the counter at its max and the tool failing and the confidence score landing under the threshold, all on the same run.

More evals do not fix the math. 100,000 samples still leave about 90% of a 20-variable space unexamined, and every variable you add doubles the space while your eval budget stays flat. "Can this graph stall?" is not a testing question. It is a counting question: out of all assignments to the guard conditions, how many leave the workflow stuck?

Count the traps instead

That is what I built SharpLogic to do. It takes a workflow graph — nodes, edges, and the guard condition on each edge, in a LangGraph-style JSON export — and checks it before the agent runs:

Compile. Each distinct guard condition becomes a boolean variable, and the graph is encoded into CNF as a trap formula: an assignment satisfies it exactly when the workflow stalls under that assignment — a reachable node with every outgoing edge disabled, a dead end that is not a terminal node, or a cycle whose exits are all closed.
Count. A zero-dependency Rust #k-SAT counter counts the satisfying assignments exactly. No sampling. The count is the number of condition combinations under which your workflow stalls, and the solver returns witnesses — concrete assignments that produce a stall, so you can see which node gets stuck and why.
Structural pass. In parallel, a graph pass (Tarjan's SCC algorithm and friends) reports dead ends and cycles with no exit at all, which are broken regardless of what the guards say.

I verified the counter against brute-force enumeration on small graphs, where checking every assignment by hand is feasible. The counts match.

Because the output is a number and not a vibe, it can sit in a build pipeline like any other check: edit the graph, recount, and if the trap count moved off zero, look at the witness before you ship.

A worked example

Here is the critic graph from above, reduced to its skeleton:

{
  "nodes": ["Start", "Tool_Executor", "Critic", "End"],
  "edges": [
    { "from": "Start", "to": "Tool_Executor" },
    { "from": "Tool_Executor", "to": "Critic" },
    { "from": "Critic", "to": "Tool_Executor", "guard": "retry_count < 3" },
    { "from": "Critic", "to": "End", "guard": "retry_count < 3" }
  ]
}

One guard condition means two total assignments. The live endpoint returns:

total assignments: 2
trap_count: 1
witness: Deadlock at node 'Critic': reachable with every outgoing edge disabled
solve time: ~148µs

Half of this graph's condition space is a stall. Now fix the exit guard so the two branches are complementary:

{ "from": "Critic", "to": "End", "guard": "retry_count >= 3" }

Recount: trap_count: 0. One line, and the stall is not patched around — it no longer exists in the graph.

Honest limits

64 variables, locally. The counter caps at 64 guard variables. Most workflow graphs I have tried sit far below that, but a graph with hundreds of distinct conditions is out of scope today.
Exact counting is worst-case exponential. A dense random 64-variable formula takes ~6.7s. Real workflow graphs are sparse and solve in microseconds to sub-millisecond; the slowest case I have measured, a strongly coupled 44–50 variable graph, took 260–290µs. Dense adversarial formulas are not the use case and will be slow.
It analyzes the graph you give it. SharpLogic counts stall assignments for the nodes, edges, and guards in your export. If the export does not match what your runtime actually does, the count describes the export.
It does not prove anything about ETH. SharpLogic was inspired by OpenAI's October 2026 post on the exponential time hypothesis. That paper is still under expert review, and this tool neither proves it nor implements its algorithm — the engine is a conventional exact DPLL-style counter with component decomposition and memoization. The engineering stands on its own either way.

Try it

No signup, no install: paste a graph JSON into the browser trial and get the trap count and witnesses back — sharplogic.vercel.app/#try. The demo graph answers in ~150µs.

For local use:

curl -fsSL sharplogic.vercel.app/install.py | python3 -

Code, issues, and the brute-force verification tests: github.com/reacherwu/SharpLogic.

One question

Which format do you define your agent workflows in — LangGraph JSON exports, graphs defined in code, a visual builder, something else entirely? That answer decides which importer I build next, so I would genuinely like to know.

You can't buy a ChatGPT recommendation — so I built a $29 test to see if you've earned one

IdeaSniper — Wed, 21 Oct 2026 02:23:16 +0000

Two weeks ago I asked four AI engines about my own brand 168 times. They mentioned me zero times. That experiment killed my first product — an AI brand-monitoring SaaS nobody would pay for.

Here's what I learned instead, and what I built this week:

1. The recommendation slot is earned, not bought.
OpenAI opened self-serve ChatGPT ads in May 2026. But ads sit next to the answer, clearly labeled, and per OpenAI they don't influence what the answer says. When a client asks "can I pay to be recommended by ChatGPT?" the honest answer is no. Organic AI visibility is a separate discipline.

2. The money already flows through AI answers.
~50M shopping questions hit ChatGPT every day. Shopify's AI-referred orders are up 13x year-over-year. AI-referred traffic converts 42–54% better. A Scandinavian activewear store just got a real order from a customer who found them through a Perplexity citation. Sellers are already spending $349–$2,849/month on tools like Azoma to be visible here — or paying agencies even more.

3. So I built the test I wish existed: Quotalis AI Shopping Visibility Check.

Free: paste your product URL, we ask a real buying question to 2 AI engines, and show you whether your product gets recommended — and who gets picked instead. No login.
$29 one-time: the full report — 2 buying questions × 4 engines (ChatGPT, Claude, Gemini, Perplexity), competitor gap table, cited-source breakdown, and a prioritized fix checklist.

Honest by design: we query model APIs through a disclosed gateway and label every number as a directional signal. No fake scores, no "guaranteed #1" nonsense. If the engines don't mention you, the report says so — that's the point.

Free check: https://quotalis.app/free-check

I'm an indie hacker, not an agency. If you're a Shopify/DTC seller, try the free check and tell me what it got wrong — that's how the product gets better.

I'm tracking what AI engines recommend for AI search visibility — in public, updated weekly

IdeaSniper — Sat, 10 Oct 2026 13:04:07 +0000

I'm tracking what AI engines recommend for AI search visibility — in public, updated weekly

AI answers change run to run. A screenshot of "ChatGPT recommends X" proves nothing — ask again tomorrow and the list shuffles. So instead of screenshots, I'm publishing the receipts: every Monday I re-run the same test and update a public benchmark.

What I do: I ask ChatGPT, Gemini, and Perplexity the exact same question, verbatim, in a fresh free-tier session with no API keys:

Which tools monitor brand visibility and citations in AI search answers from ChatGPT, Claude, Gemini and Perplexity?

Then I record every tool named and every source each engine cited. (Claude requires login, so it's skipped rather than faked.)

This week's finding (Oct 10, 2026): Profound and Peec AI are the only tools recommended by all three engines. Otterly AI, Scrunch, and Ahrefs Brand Radar appear on two of three. Full honesty: Quotalis — my own project — isn't listed by any of them yet for category queries. That's exactly the gap this benchmark exists to track, with dates attached.

Why publish it: buyers deserve dated, reproducible data instead of marketing screenshots. And there's a second reason: AI engines cite sources, and a living public dataset with methodology is citable in a way a screenshot never is.

The full benchmark — weekly tables, the 11 sources engines actually cite, and the exact methodology so you can reproduce it — lives here: https://quotalis.app/learn/ai-visibility-tools-benchmark

Next update: Monday.

How to Check Whether AI Search Engines Can See Your Brand (Free, 20 Minutes)

IdeaSniper — Sat, 10 Oct 2026 12:51:34 +0000

How to Check Whether AI Search Engines Can See Your Brand (Free, 20 Minutes)

Disclosure: I'm the maker of Quotalis, an AI search visibility monitor — one of the tools that does this kind of checking automatically. Below is the manual version anyone can run for free, and I'll be honest about what it can and can't do.

Last week I asked four AI engines about my own brand 168 times. They mentioned me zero times. (Here's the data.) That experiment was the starting point of a question every brand owner should be asking: when people ask ChatGPT, Claude, Gemini, or Perplexity about your space, does your name show up?

Here's how to answer that question yourself, with no tools and no account.

Step 1 — Write your question set (10 minutes)

Don't ask "tell me about {brand}". That's a vanity query. Ask what your customers actually ask:

Recommendation queries (3–4): "Which tools monitor brand visibility in AI search?" / "best {your category} tools for small teams"
How-to queries (3–4): "how to track brand mentions in ChatGPT" — anything your product claims to solve
Comparison queries (2–3): "{your category} tools compared"

Mix languages if your audience is multilingual. I ran 7 English + 7 Chinese questions.

Step 2 — Run each question in each engine (5 minutes with free tiers)

Open ChatGPT, Claude, Gemini, and Perplexity (free tiers all work) and paste the same questions. One engine is not enough — engines disagree wildly about the same topic.

Step 3 — Score two things separately (5 minutes)

This is where most DIY attempts go wrong. Track mentions and citations as separate columns:

Mention = your exact brand name (or handle) appears in the answer text. Watch out for partial matches: "Wu" is not "wu的智脑团". Be strict, or you'll inflate your numbers the way I almost did.
Citation = a linked source points to your page. x.com/somebody-else doesn't count as citing you just because it's the same domain. Check the full path.

My score after 168 runs: 0 mentions, 0 citations across all four engines. A clean zero is actually useful information — it means you're starting from a true baseline, and any future movement is real signal.

Step 4 — Read what the engines cite instead

The most valuable column in my spreadsheet wasn't my own score — it was the list of domains the engines did cite for my topic: tech media, dev communities, LinkedIn posts, competitor product pages. That's your distribution map. If you want AI to cite you, you need to exist where those engines already look.

The honest limits of the manual method

Doing this by hand once gives you a snapshot. Three problems appear within a week:

Drift — engine answers change as indexes update; a one-time check goes stale fast.
Coverage — I ran 3 repetitions per question because single runs vary. Doing that manually every week gets old.
Scale — 14 questions × 4 engines × 3 runs = 168 queries. It's a lot of copy-pasting.

That's why I built Quotalis to run the same battery automatically on a weekly schedule — but you shouldn't pay for something before you know the manual version works for you.

The free 20-minute shortcut

If you want the site-side half of this without running 168 queries: Quotalis has a free AI-readiness audit — no login, no signup. It fetches your site's public files (llms.txt, robots.txt, sitemap.xml) the same way AI crawlers do and tells you what's blocking them. It's not a substitute for the mention check above — it's the other half: making sure your site is legible to the engines once they do come looking.

Run the manual battery, fix what the audit flags, then re-run the battery in a month. The difference between the two numbers is your GEO progress, measured honestly.

I write about AI search visibility experiments like this one. Follow for the next round of data.

I Compared 7 AI Search Visibility Tools (With Real Pricing). Here's the Honest Breakdown

IdeaSniper — Fri, 09 Oct 2026 01:29:43 +0000

I Compared 7 AI Search Visibility Tools (With Real Pricing). Here's the Honest Breakdown

Disclosure: I'm the maker of Quotalis (one of the tools below). I tried to make this comparison fair — every fact about competitors comes from their official site as of today, and I'll say plainly where each tool (including mine) is not the best pick.

Last week I asked four AI engines about my own brand 168 times. They mentioned me zero times. That experiment taught me something uncomfortable: if AI can't see your brand, your customers can't either.

So I went shopping for AI search visibility tools — the ones that track how often ChatGPT, Claude, Gemini, and Perplexity mention and cite your brand. Here's what I found, with real pricing pulled from official sites today (October 9, 2026).

The shortlist

Tool	Engines monitored	Entry price	Free option
Surva.ai	ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews	$49/mo	7-day trial
Foglift	ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews	$49/mo	Free tier ($0, weekly Perplexity)
Seeqly	ChatGPT, Claude, Gemini, Perplexity, Grok	$21/mo	7-day trial (card required)
Citany	8 incl. Kimi + Doubao	$49/mo	14-day trial + free brand audit
Finseo	Pick any 3 of 10 (+ regional add-ons)	Not published	None listed
Profound	Trial: ChatGPT, Gemini, AI Overviews	Enterprise custom	7-day free trial
Quotalis	ChatGPT, Claude, Gemini, Perplexity	$29/mo	Free audit, no signup

What each one is actually best at

Surva.ai — the "done" button. Its pitch is "most tools stop at monitoring, Surva.ai closes the loop": one click generates citation-optimized articles and publishes straight to WordPress/Webflow. If you want monitoring plus content production in one place, this is the most complete loop. The price reflects it ($49–$349/mo).

Foglift — the developer's pick. Free tier, public REST API, CLI, and an MCP server so your AI agent can query visibility data directly. If you live in the terminal, nothing else here speaks your language. The free tier (weekly Perplexity checks) is genuinely useful.

Seeqly — the cheapest paid entry at $21/mo, and the only one here doing grounded-vs-ungrounded answer comparison (how AI answers with vs without web grounding). Heads-up: the trial requires a card and bills on day 8. Also the only one with Hinglish/Indian regional language coverage.

Citany — the coverage king. Eight engine paths including Kimi and Doubao — the only tool here that watches Chinese AI engines. Its "Citation Intelligence" shows why competitors get cited, not just that they do. If your market includes China, shortlist this one.

Finseo — the enterprise attribution play. "Attribution from AI answer to revenue" plus 40+ integrations. Pricing isn't published (talk to sales), which tells you who it's for.

Profound — the best-funded name, now positioned as an "AI Marketer" agentic teammate. Note: their old $99/$399 self-serve tiers are gone from the pricing page — it's free trial or enterprise custom now.

Quotalis (mine) — the transparency-first indie option. $29/mo gets you weekly measurement across 4 engines. Two things I do differently: every score is reported as mean + range over 3 independent runs (never a misleading single number), and the methodology page publishes the limitations openly. Plus a free audit that needs no signup — run it on your own site in 30 seconds.

The uncomfortable question I ask about all of them (including mine)

How do you know the numbers are real? Most tools show you a single visibility score with no explanation of how it was measured. One lucky run and you look like a genius; one bad run and you're invisible. That's why I built Quotalis around 3 runs per prompt per engine with mean + range — and why I'd push any vendor on this question before paying:

How many runs back each data point?
Official APIs or UI scraping? (They answer different questions.)
What are the published limitations?

If a tool can't answer those three, you're buying a vibe, not a metric.

My honest take

Indie/small team, want honest numbers cheap: Quotalis ($29) or Foglift (free tier).
Need Chinese engine coverage: Citany, no contest.
Want monitoring + content in one loop: Surva.ai.
Enterprise with attribution needs: Finseo or Profound.
Cheapest paid seat: Seeqly at $21/mo.

No single tool wins everything. Pick the one that matches your situation — and whatever you pick, ask those three methodology questions first.

I write about AI visibility measurement in public. The 168-query experiment that started this: I Asked AI About My Brand 168 Times. It Mentioned Me Zero Times. Try the free audit on your own site — no signup.

I Asked AI About My Brand 168 Times. It Mentioned Me Zero Times.

IdeaSniper — Thu, 08 Oct 2026 07:36:26 +0000

Your customers don't Google anymore. They ask ChatGPT what to buy.

A few weeks ago I ran an experiment on myself. Fourteen questions about my own X account — who I am, what I build, whether I'm worth following — asked across ChatGPT, Claude, Gemini, and Perplexity. Three independent runs per question per engine. 168 queries total.

Strict mentions of my account: zero. Not a single one.

The problem nobody is measuring

Think about what that means at scale. Every day, buyers ask AI which CRM to pick, which agency to hire, which tool solves their problem. If your brand never appears in those answers, you don't exist for that entire channel of demand. And your competitors might.

SEO taught us to measure Google. Nobody is measuring AI answers — the fastest-growing discovery channel in history. So I built the radar I wished existed.

What I built: Quotalis

Quotalis tracks your brand's mentions, citations, and share of voice across ChatGPT, Claude, Gemini, and Perplexity — plus competitor tracking, so you can see who's eating your lunch.

The methodology, stated honestly:

Every prompt × engine cell runs 3 independent times, and reports the mean with a min-max range. Single AI responses are fluky; I never want you acting on one lucky output.
Full limitations published. Measurements route through a third-party gateway (OpenRouter) by default — I say so on the methodology page instead of pretending otherwise. Your own official API keys take priority when you bring them.
API results ≠ consumer apps. The models and their search backends change constantly. I publish that caveat rather than selling certainty.

What you can do in 30 seconds

I made a free AI-readiness audit — no signup. It checks your llms.txt, robots.txt, sitemap, and whether AI crawlers can actually reach your site. Most sites I tested fail at least one check.

Try to break it

I'm an indie developer building in public. If you run the audit or try the tracking, tell me what's missing for this to be genuinely useful to you. The harshest feedback so far has been the most valuable.

Also launching on Product Hunt today — would appreciate your eyes there too.

I built an open-source Rust memory engine that cuts AI agent tokens by 96% across 1,000+ turns

IdeaSniper — Wed, 16 Sep 2026 12:56:10 +0000

If you use autonomous coding agents (Cursor, Claude Code, Antigravity, OpenClaw, Hermes, Codex), you've likely hit these long-sprint pain points:

The "Turn 200" Amnesia: You set a database or security rule at step 10. By turn 250, the agent has completely forgotten it and breaks your build.
The "Alert Storm" Doom Loop: A test fails and dumps 100 lines of error logs. Recency bias drowns out the real root cause that happened 50 turns ago.
Runaway Token Costs: Re-sending full conversation history on turn 500+ burns 100k+ input tokens per prompt, quickly draining your wallet.

To solve this, I built and open-sourced Continuum — an ultra-lightweight, zero-dependency continuous temporal memory engine written in 100% pure standard library Rust.

Real Hardware Benchmarks (Apple M4)

Instead of stuffing 100k+ histories or spinning up heavy 2GB vector databases, Continuum maintains a strictly bounded physical memory manifold (750 slots, < 75 KB RAM).

We benchmarked Continuum using OpenAI tiktoken (cl100k_base) on an Apple M4 across real coding sessions:

Metric	Full Context Appending	Standard Sliding Window (10 turns)	Continuum (Native Rust Engine)
100-Turn Cumulative Tokens	148,522 tokens	26,450 tokens	5,896 tokens (96.03% slash)
Multi-Depth Needle Recall	5/5 (100%)	0/5 (0% - Forgotten)	5/5 (100% at Rank #1)
Rule Override & Contradiction	Ambiguous prompt conflict	❌ 0% (Forgotten)	✅ 100% (Latest override at Rank #1)
Task Success / Build Pass	100% (High cost)	❌ 0% (Broken config)	✅ 100% (Tests pass, 96% token cut)
Engine Retrieval Overhead	1.2 ~ 2.5s (full history scan)	N/A (truncated)	60.46 µs (< 0.0001s, 16,540 QPS)
End-to-End Prompt Latency	12 ~ 18s (100k token load)	1.1s (shallow window)	1.2s (compact 256-token prompt)
100,000-Step Stress Test	Process Crash (OOM)	Memory leaks	Flat 750 slots (< 75 KB RAM, 0 leaks)

The Takeaway: Sliding windows save tokens but destroy outcomes (0% task success). Continuum cuts token usage by 96%~99% while guaranteeing 100% multi-depth recall and contradiction resolution.

How It Works in 4 Bullets

Physical $O(K)$ Bounded Memory: Exactly 750 slots (< 75 KB contiguous RAM). Memory usage stays a flat line forever across 100,000 steps.
Subspace Diversity Deduplication: Eliminates alert storms without naive FIFO eviction. 500 repetitive errors collapse into minimal slots, protecting ancient root causes.
Retrospective Causal Revision & Supersession: Bypasses decay for genuine anchors, while actively suppressing stale predecessors when rules are updated/contradicted.
Zero External Dependencies: 100% pure Rust std — single standalone binary, zero GC pauses, microsecond startup.

1-Minute Quickstart (MCP)

Continuum comes with a built-in Model Context Protocol (MCP) server for Cursor, Claude Desktop, and Antigravity:


bash
# 1. Install standalone CLI
curl -fsSL https://raw.githubusercontent.com/reacherwu/continuum/main/install.sh | bash

# 2. Init in any repo (< 75 KB memory manifold)
continuum-cli init .