Existing web scrapers return bloated raw HTML instead of LLM-ready formats (Markdown/JSON), maintain opaque pricing and proxy infrastructure, and strain target website bandwidth, leading to immediate server-side blocking.
Developers and site owners struggle with existing web scraping and structured data extraction solutions, either because the pricing is high, proxy infrastructure is opaque, or the scraping bots burden website infrastructure and cause blockages.
Launch HN: Context.dev (YC S26) – API to get structured data from any website
"Seems wildly expensive, furthermore not a single mention of 'ip' on homepage?"
commentSeems wildly expensive, furthermore not a single mention of "ip" on homepage? Not using rotating ip's, residential proxies? AKA unusable for high value data.
"Great, another thing I have to block server side."
commentGreat, another thing I have to block server side. Reminds me of the image leech protections that had to be in place because bandwidth was expensive. History doesn’t repeat but rhymes as they say.
"Unclear what difference exists against Firecrawl"
commentUnclear what difference exists against Firecrawl - their team has been shipping great features extremely quickly lately, and their core offerings have become really good. I am interested in KnifeGeek though - looking for a good OTF (ultratech?)
Developers trying to ingest real-time public web data into LLMs, vectors, or structured applications quickly and cleanly.
Repeated engineering pushback regarding the high cost of existing options, complete lack of transparency over underlying proxy/IP mechanics, and frustration over poorly behaved bots causing network strain.
Unlike incumbent full-suite platforms or Firecrawl, MarkdownFirst provides upfront, explicit proxy/IP visibility, aggressive token-saving Markdown parsing, and an ethical footprint that avoids getting banned by web administrators.
A transparent, high-efficiency web scraping API built specifically for AI agents that extracts web content directly into clean Markdown or strict JSON schemas. It features explicit proxy transparency and a 'good citizen' rate-limiting protocol that respects host servers to minimize blocking.
Users express frustration with existing solutions being 'wildly expensive' for returning raw HTML. Providing pre-parsed, token-optimized Markdown saves them expensive LLM API processing costs directly, creating immediate ROI.
“Turn any URL into clean Markdown for your AI agent with full proxy transparency.”
A transparent, high-efficiency web scraping API built specifically for AI agents that extracts web content directly into clean Markdown or strict JSON schemas. It features explicit proxy transparency and a 'good citizen' rate-limiting protocol that respects host servers to minimize blocking.
Target AI developer communities on Hacker News, X, and Reddit (r/LocalLLaMA, r/LanguageTechnology), emphasizing proxy transparency and token reduction benchmarks.
Established players dominate developer mindshare, requiring rapid execution and ultra-clear differentiation on pricing and proxy transparency.
Providing transparent, high-quality IP rotation may run high server costs if users execute massive concurrent scrapes on lower-tier pricing plans.
If the crawler does not accurately enforce polite rate limits, it will be actively blocked by target site administrators, degrading data reliability.
Run an Investment Memo to get a structured Go / No-Go verdict, competitor landscape, unit economics, and a 90-day validation roadmap for this opportunity.
Generate an investment memoThis opportunity scores well above the median for ideas surfaced by MonetScope, with a validation sub-score of 8/10 against 4 independently sourced evidence signals. A "strong" rating in this band typically means the pain signal is consistent and recurring across multiple discussions, but one of the three pillars (severity, willingness to pay, or competitor weakness) is somewhat softer than top-tier opportunities. Founders evaluating this should focus customer discovery on the softest pillar first — confirming the gap before committing engineering time to a build.
It sits at the intersection of "ai-powered", "automation", "data-management", which makes it relevant to a specific subset of founders rather than a generic horizontal opportunity. SaaS opportunities at this stage tend to win on the strength of their initial wedge — a single workflow that the target user runs every week, where the existing solution is either spreadsheets, a clunky incumbent feature, or a manual process they hate. The build cost is moderate; the distribution cost is everything. The MonetScope pipeline surfaces this category alongside other saas signals, which is why it appears here rather than in a generic "trending ideas" feed.
Scores are derived from real forum discussions across Reddit, Hacker News and X, weighted by evidence volume and signal quality. How scoring works
MonetScope does not generate ideas from a language model's imagination. Every opportunity on this site is anchored to specific source posts and comments from real public discussions — typically on Reddit, Hacker News, or X — where actual users describe the pain in their own words. The AI's role is structuring, scoring, and grouping those signals into a navigable opportunity, not inventing the problem.
MonetScope's spider pipeline runs continuously and surfaces opportunities as new evidence accumulates. The "Updated" date in the header reflects the most recent re-scoring of this specific opportunity. Most saas opportunities visible in the public catalog draw from discussions in the last 30-60 days; older signals are de-prioritized because user pain shifts faster than most founders assume.
Overall score is a composite across six dimensions — pain, urgency, willingness to pay, market size, defensibility, and execution ease — designed to give a single number for triage. Validation score is narrower: it asks "how cleanly does the same signal repeat across independent sources?" An opportunity can score high on overall but lower on validation when one or two large discussions dominate the evidence; conversely, validation can be high on a smaller-overall idea where the signal is consistent but the addressable market is modest.
