--- title: "Hackathon — $500 per team in Amass API credits for the Stockholm Longevity x AI Hackathon" description: "Amass is a tooling partner of the Stockholm Longevity x AI Hackathon (Epicenter Stockholm, Sep 11–13). Every team gets $500 in API credits to build longevity AI on cited evidence: six cross-linked Cores over literature, trials, drugs, genes, FDA + EMA regulatory, and patents. Track-by-track API recipes inside." url: https://amass.tech/stockholm-longevity-hackathon --- # Hackathon — $500 per team in Amass API credits for the Stockholm Longevity x AI Hackathon Stockholm Longevity x AI Hackathon · Epicenter · Sep 11–13, 2026 # Build longevity AI on cited biomedical evidence. Amass is a tooling partner of the hackathon. Every team gets **$500 in Amass API credits**. It is one REST API across six cross-linked Cores: biomedical literature, clinical trials, drugs, genes, FDA + EMA regulatory, and patents. The organisers’ advice is **“build for evidence, not theatre”**. That is what this engine returns: full records, typed schemas, every answer cited to source. Find below how to claim your team’s credits, a [gallery of apps to fork](https://platform.amass.tech/app-gallery), and a copy-paste API recipe for each track. [Claim your team’s $500 →](https://platform.amass.tech)[Jump to your track ↓](#tracks) Sign up with your hackathon email, then redeem your team’s promo code. We share the code at the Amass talk **Friday 16:45**, or message Henrik or Jörn any time. Valid until **20 September 2026**. Questions? [hello@amass.tech](mailto:hello@amass.tech). your agent · amass api you ▸Is metformin a credible geroprotector — and what is it actually approved for? 1. trialcoreMILESAMTC\_5qsK… NCT02432287 · Phase 4 · Albert Einstein · N=16 · completed, results posted 2. trialcoreANTHEMAMTC\_ScJv… NCT04264897 · Phase 3 · N=166 · completed 2025, results posted 3. regulatorycoreRiomet (metformin)AMRC\_Pytg… FDA label · indication: type 2 diabetes · no ageing indication on record 4. biomedcoreDNA Methylation Clocks in AgingAMBC\_IJRR… Molecular Cell 2018 · 670 citations · JuFo 3 — the endpoint layer ✓ Partly. Two completed RCTs (MILES, ANTHEM) tested metformin against ageing biology and posted results, but every FDA and EMA authorization is for type 2 diabetes. A longevity claim has to stand on the primary evidence, cited. trial → trial → label → literature — four calls, every hop cited. The offer ## Claim your team’s $500 in API credits Sign up with the **same email** you registered for the hackathon with, then redeem the promo code we hand out at the event — **one code per team, worth $500.** 01 ### Sign up with your hackathon email Create an account at [platform.amass.tech](https://platform.amass.tech) using the email you signed up for the hackathon with. 02 ### Redeem your team’s promo code We give out the code at the Amass talk (**Friday 16:45**) — or ask Henrik or Jörn directly. Redeem it once per team for **$500**, valid until **20 September 2026**; check the balance via GET /v1/credits/api-credits. 03 ### Create a key & build Open [API Keys](https://platform.amass.tech/api-keys), click Create API Key (starts with amass\_, shown once), and make your first call. Need the code? Get it at the Amass talk on Friday, or from Henrik or Jörn on the event WhatsApp, [Discord](https://discord.gg/sEGaBHMhWa), LinkedIn, or [hello@amass.tech](mailto:hello@amass.tech). What $500 buys **≈ 10,000 searches** or **50,000 record fetches**. Search is $0.05 per 20 results; get-by-id and lookup are $0.01 each. Plenty of headroom for a team’s weekend. Get started ## From key to first response in a minute Everything lives at [platform.amass.tech](https://platform.amass.tech). One base URL, one auth header, JSON back. bash ``` # base: https://api.amass.tech/api/v1 # auth: Authorization: Bearer amass_YOUR_KEY (every request) curl "https://api.amass.tech/api/v1/cores/trialcore/records\ ?query=metformin+aging+longevity&limit=5" \ -H "Authorization: Bearer amass_YOUR_KEY" ``` response · 200 OK ``` { "data": [ { "amassId": "AMTC_…", "briefTitle": "Metformin in Longevity Study (MILES).", "phase": "PHASE4", "overallStatus": "COMPLETED", "sponsorName": "Albert Einstein College of Medicine", "enrollment": 16 } ] } ``` [ ### Platform & keys Sign up, create API keys, check your credit balance. Open](https://platform.amass.tech)[ ### API reference Interactive docs for all six Cores, every param and field. Open](https://platform.amass.tech/api-reference)[ ### Quickstart Your first cited response in under five minutes. Open](https://platform.amass.tech/documentation/getting-started/quickstart)[ ### LLM quick reference Self-contained, paste into your agent’s system prompt. Open](https://platform.amass.tech/documentation/for-ai-agents/llm-quick-reference)[ ### Starter agent · Python A runnable natural-language agent with the API loop built in. Open](https://github.com/amass-technologies/public-amass-platform-starter)[ ### Starter agent · TypeScript The same agentic loop in TypeScript, ready for a Node or web app. Open](https://github.com/amass-technologies/public-amass-platform-starter-ts)[ ### OpenAPI spec Machine-readable: code-gen, Postman, type inference. Open](https://api.amass.tech/api/doc/openapi.json) Agents or apps ## Not just agents — build apps, too The tracks below are agent recipes. But the same REST API — same key, same cited records — just as readily powers a small, interactive app: a geroscience trial atlas, a longevity-claim checker, a target-to-approval explorer. Many in the gallery were built with [Lovable](https://amass.tech/life-science-api) — also in the hackathon tooling stack — in an afternoon. Fork one, point it at your key, and remix. [Browse the app gallery →](https://platform.amass.tech/app-gallery)[No-code recipes](https://amass.tech/life-science-api) platform.amass.tech/app-gallery Research #### From Gene to Approval One gene → every drug, trial, and FDA/EMA approval behind it. Research #### Graveyard & Garden 25 years of late-stage trials — halted or still alive. Research #### Clinical Trial Atlas Where a drug is tested, and which regions are enrolling. Research #### Indication Timeline A drug across its Phase-3 indications and approvals. Regulatory #### Transatlantic Divergence Desk FDA versus EMA outcomes, side by side. Clinical #### Pipeline Monitor A weekly digest of new Phase 2/3 trials by sponsor. Nineteen starter apps and counting, across research, regulatory, and clinical — fork, remix, and ship your own on your team’s $500 in credits. The data ## Six cross-linked Cores Same auth, same error format, same rate limits across all of them. Every record cross-links to the others, so a paper, trial, drug, gene, and approval all resolve to the same entity. ### BiomedCore 40M+ /cores/biomedcore/records Peer-reviewed biomedical literature from PubMed and PubMed Central. Every record includes MeSH terms and IDs, publication types (RCT, meta-analysis, systematic review), JuFo 0–3 journal quality tier, citation count, DOI, PMID, and PMCID. Optional: full text for PMC articles, ORCID-disambiguated authors with ROR-mapped affiliations, and links to the trials each paper describes in TrialCore. ### TrialCore 1.2M+ /cores/trialcore/records Clinical trial records from ClinicalTrials.gov and international registries. Includes protocol data, eligibility criteria, primary and secondary endpoints, sponsor details, arm groups, recruitment status, NCT ID, and the full lifecycle — start, completion, results posting, and why stopped. Outcome measurements are available as structured values per arm, not PDF blobs. Records link back to the publications that describe them in BiomedCore. ### DrugCore 22K+ /cores/drugcore/records ChEMBL-derived drugs and molecules, harmonized. Names, trade names, and synonyms; chemical structures with InChIKey and SMILES; modality classification; and highest clinical stage from preclinical through approval. Each drug cross-links to the trials, papers, and regulatory authorizations it appears in. ### RegulatoryCore FDA + EMA /cores/regulatorycore/records FDA and EMA regulatory data on one unified schema. FDA application records, drug labels, review documents, and approval letters; EMA EPARs, assessment reports, and SmPCs. Each record carries a unified authorization status, designations (Breakthrough Therapy, PRIME, Accelerated Approval), and orphan status — and cross-links to the drug it covers in DrugCore. ### GeneCore 43K+ /cores/genecore/records 43,000+ harmonized human gene records from HGNC, NCBI, UniProt, and Ensembl. Druggability and tractability assessments, target safety data, genetic constraint via gnomAD v4.0 (pLI, LOEUF), and cellular essentiality from CRISPR dependency screens. Each gene cross-links to the drugs that target it in DrugCore, the trials that study it in TrialCore, and the papers that cite it in BiomedCore — from gene to clinical evidence in one call. ### PatentCore 16M+ /cores/patentcore/records Patent publications with English-preferred full text — title, abstract, claims, and description — plus bibliographic metadata (publication and application numbers, jurisdiction, kind code, family id), CPC/IPC classifications, inventors and assignees, four separate dates, and citation lineage. Search returns one publication per family, the most relevant member, and every record cross-links to the drugs it mentions in DrugCore and the papers it cites in BiomedCore. Tracks ## An API recipe for every track Inspiration, not coverage. Each command below was run against the live API and the responses are real (trimmed for width). Jump to your track: [1 · Longevity Biology × AI](#longevity-biology)[2 · AI Infrastructure — Nebius](#ai-infrastructure)[3 · Communication, Trust & Policy](#communication-trust)[4 · Healthspan & Clinical Translation](#healthspan)[5 · Open / Wildcard](#wildcard) Track ### 1 · Longevity Biology × AI Ageing clocks & biomarker inference · target triage for the biology of ageing · AI-supported drug repurposing · in-silico trials — defensible applications of AI to how we age. BiomedCoreGeneCoreDrugCore Ground an ageing-clock build in the actual methodology, ranked by journal quality and citations, cited to source. bash ``` curl "https://api.amass.tech/api/v1/cores/biomedcore/records\ ?query=epigenetic+clock+DNA+methylation+biological+age\ &minJournalQualityJufo=2&limit=4" \ -H "Authorization: Bearer amass_YOUR_KEY" ``` response · 200 OK · 4 records, JuFo ≥ 2 ``` TITLE JOURNAL YR CITED DNA Methylation Clocks in Aging: Categories, Causes… Molecular Cell 2018 670 Tracking the Epigenetic Clock Across the Human Life… J Gerontol A 2019 139 Epigenetic Aging: More Than Just a Clock When It… Cancer Research 2020 129 Retro-age: A unique epigenetic biomarker of aging… Aging Cell 2024 26 ``` Triage an ageing target in one call: SIRT1 comes back with clinical-precedent tractability, genetic constraint, and curated safety signals, each with the numbers to defend the pick. bash ``` curl "https://api.amass.tech/api/v1/cores/genecore/records\ ?query=SIRT1&limit=1" \ -H "Authorization: Bearer amass_YOUR_KEY" ``` response · 200 OK · GeneCore SIRT1 ``` symbol SIRT1 location 10q21.3 LOEUF 0.527 pLI 0.90 targetClass Epigenetic regulator › Eraser › HDAC class III tractability small molecule: Advanced Clinical Structure with Ligand · High-Quality Ligand · Druggable Family safetyLiabilities • regulation of catalytic activity ToxCast ``` Also try For the repurposing half: DrugCore resolves “rapamycin mTOR inhibitor” to harmonized candidates with clinical stage (ridaforolimus at Approval, vistusertib and sapanisertib at Phase 2). Cross-link each hit into TrialCore for the trials that ran it and BiomedCore for the papers behind it. Track ### 2 · AI Infrastructure — Nebius A performant, scalable longevity-compute solution on the Nebius stack. Whatever you serve needs a grounded data layer behind it. Amass is one REST API with typed schemas and predictable limits, built to sit inside a pipeline. All six CoresOpenAPI Hydrate a feature store or vector index with harmonized records. Same query, same result, safe to cache hard. bash ``` curl "https://api.amass.tech/api/v1/cores/drugcore/records\ ?query=rapamycin+mTOR+inhibitor\ &drugType=SMALL_MOLECULE&limit=5" \ -H "Authorization: Bearer amass_YOUR_KEY" ``` response · 200 OK · cache-friendly ``` NAME CHEMBL STAGE Ridaforolimus CHEMBL2103839 APPROVAL RG-7603 CHEMBL2331680 PHASE1 Vistusertib CHEMBL2336325 PHASE2 Palomid-529 CHEMBL2141712 PHASE1 AZD-8055 CHEMBL1801204 PHASE1/PHASE2 ``` Also try The plumbing that matters at scale: 60 requests / 60s with X-RateLimit-Remaining and X-Amass-Credit-Cost on every response, a flat { data } envelope, batch /lookup endpoints for external IDs, and a machine-readable OpenAPI spec at api.amass.tech/api/doc/openapi.json for code-gen and typed clients. Track ### 3 · Communication, Trust & Policy Products, tools, or campaigns that make longevity science understandable and credible — misinformation checkers, claim graders, responsible-adoption workflows. RegulatoryCoreBiomedCoreTrialCore What is metformin actually approved for? Sweep the unified FDA + EMA record: every authorization is type 2 diabetes, none is longevity. That gap is what a claim checker tests headlines against. bash ``` curl "https://api.amass.tech/api/v1/cores/regulatorycore/records\ ?query=metformin&limit=4" \ -H "Authorization: Bearer amass_YOUR_KEY" ``` response · 200 OK · FDA + EMA, unified ``` NAME AGENCY STATUS ACTIVE SUBSTANCE · INDICATION Riomet FDA WITHDRAWN_VOLUNTARY Metformin HCl · type 2 diabetes Jentadueto EMA ACTIVE Linagliptin;Metformin · type 2 diabetes Kazano FDA ACTIVE Alogliptin + metformin · type 2 diabetes Synjardy EMA ACTIVE Empagliflozin;Metformin · type 2 diabetes # no metformin authorization carries an ageing or longevity indication ``` Also try For the claim grader: BiomedCore carries isRetracted, journalQualityJufo (0–3), and citation counts on every record — enough to separate “one small study” from settled literature. TrialCore's hasResults and whyStopped distinguish tested from merely registered. Track ### 4 · Healthspan & Clinical Translation Clinician-facing tools, preventive-health workflows, and credible pathways from data or research to real-world healthspan applications. TrialCoreRegulatoryCore The human evidence for metformin as a geroprotector, in one call: two completed RCTs with posted results, and the next one still recruiting. bash ``` curl "https://api.amass.tech/api/v1/cores/trialcore/records\ ?query=metformin+aging+longevity&limit=4" \ -H "Authorization: Bearer amass_YOUR_KEY" ``` response · 200 OK · geroscience RCTs ``` TRIAL REGISTRY ID PHASE STATUS N RESULTS MILES NCT02432287 PHASE4 COMPLETED 16 posted ANTHEM NCT04264897 PHASE3 COMPLETED 166 posted AGE-M NCT06459310 PHASE2 RECRUITING 130 — (Padova) NCT01765946 PHASE4 COMPLETED 38 — ``` Healthspan is more than drugs. TrialCore covers the WHO ICTRP registries too, so exercise and nutrition interventions from Thailand to Mexico are one query away. bash ``` curl "https://api.amass.tech/api/v1/cores/trialcore/records\ ?query=sarcopenia+older+adults\ &overallStatus=COMPLETED&hasResults=true&limit=4" \ -H "Authorization: Bearer amass_YOUR_KEY" ``` response · 200 OK · beyond ClinicalTrials.gov ``` REGISTRY ID INTERVENTION N COUNTRY tctr TCTR20230703001 resistance exercise + whey protein 25 MY isrctn ISRCTN48485253 tai chi vs resistance exercise 120 MX isrctn ISRCTN17269170 social-media exercise programme 35 CN tctr TCTR20231005004 virtual-reality exercise 58 TH ``` Also try To keep a clinician tool inside the label: RegulatoryCore parses FDA labels and EMA SmPCs into addressable sections, so a workflow can quote the exact contraindication passage, id and all, instead of a summary. Track ### 5 · Open / Wildcard Propose your own challenge at the frontier of AI and longevity biotech. By definition you already know the problem; the whole engine is open, and the strongest entries usually chain Cores together. All six CoresCross-Core links Cross-Core links let one entity resolve across all six Cores, every hop cited to source. bash ``` curl "https://api.amass.tech/api/v1/cores/genecore/records\ ?query=SIRT1&include=referencesDrugCore&limit=1" \ -H "Authorization: Bearer amass_YOUR_KEY" ``` response · 200 OK · one gene → the whole thread ``` SIRT1 → referencesDrugCore: the drugs that target it │ ├─ each drug → referencesTrialCore (the trials that ran it) ├─ each drug → referencesBiomedCore (the papers behind it) └─ each drug → referencesRegulatoryCore (FDA / EMA labels) gene → drug → trial → paper → approval, in one thread. all cited. ``` Playbook ## Nine ways to get further, faster 40% of the judging rubric is impact plus evidence and responsible use, and a demo that cites its sources scores on both. These patterns separate a build that lands from one that fights its own plumbing. 01 ### Build an agent, not a script. Let the model call the API in a loop: search broadly, filter, drill into a record, repeat. Our open-source starter agents (Python and TypeScript) ship exactly this loop; fork one and go. 02 ### Ground every claim in a record. The whole point is answers you can trust: each result carries a stable Amass id and links back to its source. No hallucinated PMIDs or NCT numbers. 03 ### Cross-Core is your edge. A gene resolves to the drugs that target it, the trials that ran them, the papers behind them, and the FDA/EMA labels that cover them. Follow the links, one call at a time. Treat an empty reference array as “none recorded,” not “no evidence.” 04 ### Query, do not scrape. PubMed, ClinicalTrials.gov, WHO ICTRP, ChEMBL, HGNC, UniProt, FDA & EMA are already normalised and cross-linked. Spend your weekend on the idea, not the ETL. 05 ### Narrow with filters. journalQualityJufo, phase, maxConstraintLoeuf, tractability, agency… tighter queries mean higher signal and fewer credits burned. Skip include=fulltext unless you truly need it. 06 ### Be nice to the rate limit. 60 requests / 60s, keyed by user + org. On a 429, read Retry-After and back off exponentially. Every response carries X-RateLimit-Remaining and X-Amass-Credit-Cost. 07 ### Read the data envelope. Every response is wrapped in { "data": … }; errors come back in a different shape under "error". Read results from data, and check for error before you parse. A batch lookup can even fail item-by-item. 08 ### Convert external IDs first. Get-by-id endpoints take Amass IDs only (AMBC\_, AMTC\_, AMDC\_, AMRC\_, AMGC\_). Holding PMIDs, DOIs, NCTs, ChEMBL, or gene IDs? Batch them through the /lookup endpoints, then fetch. 09 ### Cache, and mind pagination. Same query, same result. Memoise locally so a weekend of iterating does not re-spend credits. And there is no pagination: 300 results max per search, so narrow with filters instead of trying to page. Prototype in Claude ## Sketch your agent in Claude before you write app code. Connect the Amass MCP in Claude.ai, Claude Code, Cursor, ChatGPT, or Codex and query all six Cores in natural language. Explore the data, find the filters that work, then drop the same queries into the REST API in your build. In Claude Code, install the Amass SKILL.md and let the model wire up your data layer for you — a quick path from “what data is even in here?” to a working prototype. [Connect the MCP](https://amass.tech/mcp)Starter agent:[Python](https://github.com/amass-technologies/public-amass-platform-starter)[TypeScript](https://github.com/amass-technologies/public-amass-platform-starter-ts) Go further ## More ways to put the credits to work Not a coder on your team, or just want to move faster? Pick a lane. [ ### Fork an app from the gallery Nineteen starter apps — trial atlas, indication timeline, gene-to-approval — built on the same API. Fork one and remix it with your key. Browse the gallery](https://platform.amass.tech/app-gallery)[ ### No-code builds Paste your key into Lovable, Streamlit, or an n8n HTTP node and ship a filterable trial tracker or literature app in an afternoon. See examples](https://amass.tech/life-science-api)[ ### Claude Code + SKILL.md Install the Amass skill and let Claude Code build your Amass data layer, then iterate on the science instead of the SDK. Get the skill](https://amass.tech/mcp)[ ### Demo it live in Claude With the MCP connected, your agent’s answers come back cited in the chat, a clean, judge-friendly demo with zero deploy. How it works](https://amass.tech/mcp)[ ### Chain the Cores Strong entries fan out: gene → drug → trial → paper → label. Follow the reference fields and cite every hop. See the wildcard track](#tracks)[ ### Share on Discord Show what you are building with life-science AI builders, and tag the Amass team for a boost. Join now](https://discord.gg/sEGaBHMhWa) On the ground ### Meet the Amass mentors on-site [Henrik Jensen](https://www.linkedin.com/in/jensenhenrik/) (co-founder & CEO) and [Jörn Petersen](https://www.linkedin.com/in/petersenjoern/) (AI & data engineer) are your in-person mentors. Catch the Amass talk **Friday 16:45**, then find them in the **Saturday mentor rotations** for the promo code, API help, or to talk through your build. Off-site they answer on the event WhatsApp, Discord, LinkedIn, and email. [Meet the full Amass team →](https://amass.tech/about#team) [Henrik · LinkedIn](https://www.linkedin.com/in/jensenhenrik/)[Jörn · LinkedIn](https://www.linkedin.com/in/petersenjoern/)[Email](mailto:hello@amass.tech) ## Go build for longer, healthier lives Redeem your team’s $500 in credits, wire up a Core, and let an agent do the reading. Build for evidence, not theatre. We cannot wait to see what you make. [Claim your team’s $500 →](https://platform.amass.tech)[API reference](https://platform.amass.tech/api-reference)[Connect the MCP](https://amass.tech/mcp) One promo code per team, from the Friday talk or from Henrik and Jörn · valid until 20 September 2026 · questions? [hello@amass.tech](mailto:hello@amass.tech)