Somehow "clerk" is on my ublock origin blocklist and therefore the whole website is not loading. I didn't add "clerk" to the blocklist so it must've been added by one of the blocklists that ublock origin is subscribed to, so there must be a good reason why "clerk" is on that blocklist.<p>When building a product for medical audience which might care a lot about privacy maybe don't use components which are shady enough that they end up on blocklists.<p>Edit:<p>> Why no Vector DB? In medicine, "freshness" is critical. If a new trial drops today, a pre-indexed vector store might miss it. My real-time approach ensures the answer includes papers published today.<p>This is total rubbish - did you talk to a single medical practitioner when building this? Nobody will do new treatments on their patients if a new paper was "published" (whatever that means, just being added to some search index). These people require trusted source, experimental treatment is only done for private clients who have tried all other options.
Thanks for the feedback—this is helpful.<p>1. Re: Clerk/uBlock: You were spot on. The default Clerk domain often gets flagged by strict blocklists. I just updated the DNS records to serve auth from a first-party subdomain (clerk.getevidex.com) to resolve this. It should be working now.<p>2. Re: Freshness & 'Rubbish': You are absolutely right that standard of care doesn't (and shouldn't) change overnight based on one new paper.<p>However, the decision to ditch the Vector DB for Live Search wasn't about pushing 'experimental treatments'—it was about Safety and Engineering constraints:<p>Retractions & Safety Alerts: A stale vector index is a safety risk. If a major paper is retracted or a drug gets a black-box warning today, a live API call to PubMed/EuropePMC reflects that immediately. A vector store is only as good as its last re-index.<p>The 'Long Tail': Vectorizing the entire PubMed corpus (35M+ citations) is expensive and hard to keep in sync. By using the search APIs directly, we get the full breadth of the database (including older, obscure case reports for rare diseases) without maintaining a massive, potentially stale index.<p>The goal isn't to be 'bleeding edge'—it's to be 'currently accurate'.
a good system (like openevidence) indexes every paper released and semantic search can incredible helpful since the the search api of all those providers are extremely limited in terms of quality.<p>now you get why those system are not cheap. keeping indexes fresh, maintaining high quality at large scale and being extremely precise is challenging. by having distributed indexes you are at the mercy of the api providers and i can tell you from previous experience that it won't be 'currently accurate'.<p>for transparency: i am building a search api, so i am biased. but i also build medical retrieval systems for some time.
Appreciate the transparency and the insight from a fellow builder.<p>You are spot on that maintaining a fresh, high-quality index at scale is the 'hard problem' (and why tools like OpenEvidence are expensive).<p>However, I found that for clinical queries, Vector/Semantic Search often suffers from 'Semantic Drift'—fuzzily matching concepts that sound similar but are medically distinct.<p>My architectural bet is on Hybrid RAG:<p>Trust the MeSH: I rely on PubMed's strict Boolean/MeSH search for the retrieval because for specific drug names or gene variants, exact keyword matching beats vector cosine similarity.<p>LLM as the Reranker: Since API search relevance can indeed be noisy, I fetch a wider net (top ~30-50 abstracts) and use the LLM's context window to 'rerank' and filter them before synthesis.<p>It's definitely a trade-off (latency vs. index freshness), but for a bootstrapped tool, leveraging the NLM's billions of dollars in indexing infrastructure feels like the right lever to pull vs. trying to out-index them.
This sounds like a cookie cutter ChatGPT reply.
Now it is loading. You are still in violation of GDPR rules by including a SVG file with the google logo from the clerk.com domain and a css file from tailwindcss.com - both are tracking users. There is no privacy policy on your page. The privacy policy should include a list of companies you share my visitor data with and what kind of data is shared, and how I can deny sharing that data.
Fair point on the Privacy Policy link. That definitely slipped through the cracks in the launch rush. I just pushed a fix to add it to the footer now.<p>Re: the trackers: The SVG is just the icon inside the Clerk login button, but you're right that loading Tailwind via CDN isn't ideal for strict GDPR IP-masking. I'll look into self-hosting the assets to clean that up.
I like your approach of "smart routing" but using regex/keywords based approach has a problem that it does not captures semantic similarity of keywords so search with similar intents are missed, how are you handling it? or you dont need to handle it since it is for domain experts and they are likely to search based on keywords(dictionary)?
I'm working on building an AI agent that creates queries over a time-series database focused on financial data. For example, it can quantify Federal Reserve reports and generate a table showing how SPY reacted 30 minutes after, at EoD, at the next day’s open, and at the next day’s EoD. It will plan the database query and then query the data from a materialized view. It is magic!<p>How would biomedical researchers use tons of time-series data? A better question is: what questions are biomedical researchers asking with time-series data? I'm a lot more interested in generalized querying over time-series data than just financial data. What would be a great proof of concept?
That sounds like a fascinating project.<p>To answer your question: In the biomedical world, the 'Time-Series' equivalent is Patient Telemetry (Continuous Glucose Monitors, ICU Vitals, Wearables).<p>The Question Researchers Ask: 'Can we predict sepsis/stroke 4 hours before it happens based on the velocity of change in Heart Rate + BP?'<p>Right now, Evidex is focused on the Unstructured Text (Literature/Guidelines) rather than the structured time-series data, but the 'Holy Grail' of medical AI is eventually combining them: Using the Literature to interpret the Live Vitals in real-time.
Out of curiosity, what's the prioritization of evidence (RTC Metanalysis > RTC > observational ) etc, and what's the end user benefit over a tool like OpenEvidence? You mention that other tools are expensive, slow, or increasingly heavy with pharma ads, but OpenEvidence for now seems to be pretty similiar with offerings, speed, and responses. What's your pitch as to why one should prefer this?
Great questions.<p>1. Prioritization: I instruct the model to prioritize evidence in this hierarchy: Meta-Analyses & Systematic Reviews > RCTs > Observational Studies > Case Reports. It explicitly deprioritizes non-human studies unless specified.<p>2. Why not OpenEvidence? OE is excellent! But we made two architectural choices to solve different problems:<p>'Long Tail' Coverage: OE relies on a pre-indexed vector store, which often creates a blind spot for niche/rare diseases where papers aren't in the 'Top 1% of Journals.' Because Evidex queries live APIs, we catch the obscure case reports that static indexes often prune out.<p>Workflow: OE is a 'Consultant' (Q&A). Evidex is a 'Resident' (Grunt work). The 'Case Mode' is built to take messy patient histories and draft the actual documentation (SOAP Notes/Appeals) you have to write after finding the answer.
FYI, You are using Clerk in development mode
Great project. Want to contact me when you'd like to talk? I do software engineering for clinicians at a health care organization, and I'd love to have my teams try your work in their own contexts. Email joel@joelparkerhenderson.com.
Out of curiosity, did you actually see any pharma ads on OpenEvidence?
Great question. I haven't seen banner ads on OpenEvidence yet, but the 'hidden tax' of free tools is often Publisher Bias.<p>Users have noted that some current tools heavily overweight citations from 'Partner Journals' (like NEJM/JAMA) because they index the full text, effectively burying better papers from non-partner journals in the vector retrieval.<p>My goal is strictly Neutral Retrieval. By hitting the PubMed/OpenAlex APIs live, Evidex treats a niche pediatric journal with the same relevance weight as a major publisher, ensuring the 'Long Tail' of evidence isn't drowned out by business partnerships.
this might be interesting: <a href="https://www.theinformation.com/articles/chatgpt-doctors-startup-doubles-valuation-12-billion-revenue-surges" rel="nofollow">https://www.theinformation.com/articles/chatgpt-doctors-star...</a><p>> $150M RR on just ads, +3x from August. On <1M users.<p>source: <a href="https://x.com/ArfurRock/status/1999618200024076620" rel="nofollow">https://x.com/ArfurRock/status/1999618200024076620</a>
All such custom sites are increasingly unnecessary since modern thinking AIs like ChatGPT 5.2 Extended and Gemini 3 Pro do an incredible job surfacing good papers. In my experience, the benefit comes from using multiple AIs because they all have blind spots, and none is pareto optimal.<p>As a patient, sometimes I don't want the AI to have my entire medical history, as this lets me consider things from different angles. For each chat, I give it the reconstructed history that I think is sufficient. I want it to be an explorer more than a doctor.
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