How do systems (or people) detect when a text is written by an LLM
Hello guys, just curious about how can people or systems (computers) detect when a text was written by an LLM. My question is mainly focused to if there is some API or similar to detect if a text was written by an LLM. Thanks!!!
FL score
out of 100
Verdict
high confidence
Competition
9
competitors found, emerging market, funded players
Trend
No signal yet
An AI detection API that aims to improve on the current high false positive rates and inconsistency of existing tools, targeting educators, publishers, and content platforms.
The pain
The gap
Build angle
Strengths
- The problem of AI content detection is real, severe, and widely discussed.
- The market for AI detection tools is large and rapidly growing.
- Users are clearly willing to pay for effective solutions.
- Specific user frustrations provide clear targets for product improvement.
Risks
- The market is highly crowded with both funded startups and established players, some with institutional dominance.
- Building a technically superior AI detection model that significantly outperforms incumbents is extremely complex for a solo builder.
- High false positive rates are an inherent challenge in AI detection, making 'believability' of a superior solution low without significant proof.
- Lack of a distinct, validated niche beyond 'a better API' makes differentiation difficult.
Next steps
- Deeply research a hyper-specific niche (e.g., 'AI detection for [specific content type] in [specific industry]').
- Interview users within that niche to validate their specific pain points and current workarounds.
- Explore novel technical approaches (e.g., watermarking, stylistic analysis) that could offer a defensible advantage over existing LLM output analysis.
- Build a tiny MVP focused on proving superior accuracy for *one* specific complaint within that niche, rather than a general-purpose API.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 26
- Solution gap
- 11
- Willingness to pay
- 20
- Buildability
- 3
High demand for AI detection exists due to clear, severe pain points, and users are willing to pay for solutions. However, the market is crowded with funded incumbents, and building a genuinely superior product that addresses specific complaints with high accuracy is a significant technical challenge for a solo founder.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Strong market demand and growth potential, but the complexity of achieving a superior, believable solution in a crowded space makes differentiation and solo execution difficult.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Clear market problem and monetization path, but high technical complexity, intense competition, and lack of a distinct niche make it challenging for a solo builder.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Strong value proposition but a lack of specific target audience and a high assumption risk regarding technical superiority make it difficult to validate and launch as a micro-SaaS.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Strong underlying demand for AI detection but lacks a desperate, specific wedge and faces high technical hurdles to outperform existing solutions.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Deeply research a hyper-specific niche (e.g., 'AI detection for [specific content type] in [specific industry]').
- Interview users within that niche to validate their specific pain points and current workarounds.
- Explore novel technical approaches (e.g., watermarking, stylistic analysis) that could offer a defensible advantage over existing LLM output analysis.
- Build a tiny MVP focused on proving superior accuracy for *one* specific complaint within that niche, rather than a general-purpose API.
Who is already there, emerging market
Originality.ai offers a suite of AI tools for writers, students, editors, marketers, web publishers, and educators, including AI detection, plagiarism checking, fact-checking, and grammar checking.
Pricing: Starts at $14.95/month or $30 for 3,000 credits (pay-as-you-go).
- GPTZeroVisit
GPTZero is an AI detection tool focused on identifying GPT-generated content, offering a free online demo, Chrome extension, and integrations with academic platforms.
Pricing: Free tier for up to 10,000 words per month (5 advanced scans). Paid plans for 150,000 to 500,000 words per month.
Copyleaks provides an AI detection API and plagiarism checker that identifies AI-generated content from various models in over 30 languages.
Pricing: $7.99/month for AI detection only, or $13.99/month for AI + plagiarism bundled. Credit-based system with significant cost implications.
Turnitin is widely used in educational institutions for plagiarism and AI content detection, integrated with LMS platforms.
Pricing: Institutional licensing only; not available for individual purchase.
- Winston AIVisit
Winston AI is an AI content detection solution that helps detect AI-generated content from various LLMs with high accuracy, also offering a plagiarism detection tool.
Pricing: Starts at $18/month.
- SaplingVisit
Sapling offers an AI content detection API as part of its broader AI writing and grammar assistant tools, providing overall and sentence-level scores.
Pricing: Free version has short text limits. API access for Team or Enterprise plans.
- ZeroGPTVisit
ZeroGPT is a free and fast AI detector for quick checks on AI-generated text.
Pricing: Free, with unlimited scans without an account.
QuillBot's AI Detector identifies text generated by major LLMs and also text refined with paraphrasers or grammar checkers, providing sentence-level analysis.
Pricing: AI detection is part of premium plans, not a free Turnitin alternative.
Grammarly offers an AI Detection API that allows organizations to evaluate the authenticity of written content by estimating the likelihood it was generated by AI tools.
Pricing: AI detection and plagiarism features are locked behind premium plans.
What they charge
Recent news
- AI May Soon Detect Cancer Just by Listening to You Speak
SciTechDaily, April 05 2026
- Tripped up by misinformation? Here's a refresher on identifying AI
PBS News, April 02 2026
- 'Soon publishers won't stand a chance': literary world in struggle to detect AI-written books
The Guardian, March 29 2026
- States Push AI Weapons Detection as Part of School Safety
Education Week, March 26 2026
- GPTZero vs Copyleaks: Which AI Detector Is More Accurate?
AmpiFire, March 17 2026
Market signals
The AI detector market is experiencing strong growth, valued at approximately USD 0.69 billion in 2025 and projected to reach USD 4.81 billion by 2033, growing at a CAGR of 27.55% from 2026-2033. This growth is driven by the rapid increase in AI-generated content across education, media, publishing, cybersecurity, and enterprises, leading to a high demand for tools that ensure content authenticity, copyright protection, and fraud prevention. North America holds the largest revenue share, with the U.S. market alone valued at USD 0.72 billion in 2025 and growing at a strong 26.4% CAGR.
What frustrates people
- High false positive rates, leading to human-written content being incorrectly flagged as AI-generated.
- Inconsistent detection results, with the same text yielding different scores across tools or even within the same tool at different times.
- Difficulty in detecting 'humanized' or 'AI-assisted' content where AI-generated text has been edited by a human.
- Limitations in accuracy for advanced AI outputs, nuanced content, or languages other than English.
- Short text limits in free versions, requiring users to copy-paste in pieces, which can lead to fragmented results.
Semble – Code search for agents that uses 98% fewer tokens than grep
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.Main features:- Token-efficient: 98% fewer tokens than grep+read- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode- Zero config: no API keys, no GPU, no external servicesInstall in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" sembleOr check our README for other installation instructions, benchmarks, and methodology:Semble: https://github.com/MinishLab/sembleBenchmarks: https://github.com/MinishLab/semble/tree/main/benchmarksModel: https://huggingface.co/minishlab/potion-code-16MLet us know if you have any feedback or questions!
AI
Postgres extension for BM25 relevance-ranked full-text search
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Consus (YC F24) – LLM gateway for companies handling sensitive data
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.Demo: https://youtu.be/6MYxJe6P4oYAerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Clau
AI
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “How do systems (or people) detect when a text is written by an LLM”.