--- title: "Signal From Tomorrow — the three science tracks at the Vienna AI hackathon" description: "Amass is a partner of HACK_002: Signal From Tomorrow, the 24-hour AI hackathon at HOIV Vienna on 26 and 27 September 2026. Every team gets $500 in Amass API credits. Inside: the three Block B science tracks explained plainly, a picture of what a finished entry looks like on each one, three starting points per track, and a prompt you can paste straight into Cursor." url: https://amass.tech/signal-from-tomorrow --- # Signal From Tomorrow — the three science tracks at the Vienna AI hackathon HACK\_002 · Signal From Tomorrow · HOIV Vienna · 26–27 September 2026 # Three science tracks, and twenty-four hours to build one of them. Amass is a partner of [HACK\_002](https://events.teloscircle.com/hackathon-02/), and every team gets **$500 in Amass API credits**. Amass is one REST API over six cross-linked Cores: 40M+ papers, 1.2M+ clinical trials, 22K+ molecules, 43K+ genes, FDA and EMA authorizations, and life-science patents. Every answer arrives with the record it came from. You do not need to be a developer to use any of it, and you do not need a biology degree to enter Block B. Each of the three tracks below opens with a picture of what a finished entry looks like, then three concrete places to start, then a prompt you can paste straight into Cursor. [Pick your track ↓](#tracks)[Claim your team’s $500 →](https://platform.amass.tech) Doors open at 12:00 on Saturday, building runs from 13:00 to 13:00, and judging follows lunch on Sunday. The venue is HOIV, Arsenalstraße 11, 1030 Vienna, with 24-hour access. Registration and the full schedule are on [Luma](https://luma.com/1ka4b1nm) and the [Telos Circle event page](https://events.teloscircle.com/hackathon-02/). Questions for us: [hello@amass.tech](mailto:hello@amass.tech). Block B · the science block Three tracks. Pick one on Saturday and you have chosen your weekend. - [B1AI for Longevity and AgeingA tool, model, analysis, or hypothesis about ageing and healthspan.](#longevity) - [B2Neural SystemsA simulation or an experiment around neural and brain-inspired systems.](#neural) - [B3AI for Scientific DiscoveryA system that speeds up one stage of the scientific process.](#discovery) Block A runs alongside it for general AI, consumer and developer tooling, and the sponsor challenges. Everything on this page is Block B. Pick a track ## Which one is yours Choosing is the first and largest decision of the weekend, so here are the three side by side. Nothing stops you from moving later, but the entries that land tend to be the ones that picked at 13:00 on Saturday and never looked up. B1 ### AI for Longevity and Ageing Choose this if You like working with real data, you can read a statistics table, or you already have opinions about ageing biology. You will build A tool, model, analysis, or hypothesis about ageing and healthspan. Judged on Held-out error, or an effect estimate with its uncertainty. Explain the sampling and the confounding, and say why the result does not establish a treatment benefit. [See the starting points](#longevity) B2 ### Neural Systems Choose this if You like dynamical systems, graphs, or simulation, and you would rather watch something behave than fit something to a table. You will build A simulation or an experiment around neural and brain-inspired systems. Judged on Pattern recall, response delay, network stability, or another metric you define, measured across a parameter sweep and against a baseline. [See the starting points](#neural) B3 ### AI for Scientific Discovery Choose this if You like building tools and agents, and you are more interested in how research gets done than in any single result. You will build A system that speeds up one stage of the scientific process. Judged on Citation support, reviewer agreement, false positives, or time saved, against a manual or simple baseline. A generated hypothesis is not a validated discovery, and saying so in the demo counts in your favour. [See the starting points](#discovery) A note on judging Block B is judged by **Constantin Convalexius** (Medical University of Vienna, Harvard) and **Lucas Mair** (Technical University of Munich, ProviGen), alongside the general panel. They will have seen the underlying literature before. The Measure line on each card above is the closest thing to a published rubric, so treat it as one. [B1 Longevity & ageing](#longevity)[B2 Neural systems](#neural)[B3 Scientific discovery](#discovery) Track B1 ### AI for Longevity and Ageing Biomarkers, ageing clocks, biological data, literature, and the research workflows around them. Public datasets are plentiful here, so this is the track with the shortest distance between sitting down and having a first result on screen. BiomedCoreGeneCorePublic datasets How it is measured Held-out error, or an effect estimate with its uncertainty. Explain the sampling and the confounding, and say why the result does not establish a treatment benefit. localhost:3000/evidence-map #### Which ageing clock says what 4 studies · 5 clocks · JuFo 2+ Four published studies down the side and five DNA methylation clocks across the top. Each cell reports whether that study found significant epigenetic age acceleration, significant deceleration, a non-significant result, or did not report the clock at all. | Finding | Horvath | Hannum | PhenoAge | GrimAge | DunedinPACE | | --- | --- | --- | --- | --- | --- | | IPF lung tissue vs healthy controlsacceleration tracks disease severityAm J Physiol Lung Cell Mol Physiol 2025JuFo 2PMID 39970931 | ↑ | ↑ | ↑ | — | ↑ | | Fast vs slow glaucoma progressionHorvath +2.93 y, Hannum +1.24 yOphthalmology 2025JuFo 3PMID 39716635 | ↑ | ↑ | ns | ↑ | — | | Blood vs brain-MRI age, twin cohortheld up in within-pair modellingBrain 2025JuFo 3PMID 39992867 | ns | ↑ | ns | ↑ | ns | | Drug-naive first-episode schizophreniadeceleration, p = 0.01BMC Psychiatry 2023JuFo 2PMID 36650462 | ↓ | ns | ns | — | — | ↑ accelerated↓ deceleratedns not significant— not reported Read the Horvath column downwards. It runs up, up, ns, down across four studies, so the clock you pick is a choice you have to defend. Three places to start #### Age-stratified biomarker panel Pick three biomarkers and plot them by age decade in NHANES. Write down every exclusion you made and say plainly what the sampling cannot tell you. The write-down is the hard part and the part that scores. NHANES · half a day #### One signature, two studies Take a single ageing-related expression signature and compare it across two GEO studies. Then find the batch effect and show it, before a judge asks whether you looked. GEO · one evening #### Evidence map of a mechanism Link a proposed ageing mechanism to the papers behind it and mark the places where they contradict each other. This is the one in the window above, and it is the least code of the three. BiomedCore · a few hours Paste this into Cursor to start prompt ``` Build me an evidence map for one ageing question. Question: does epigenetic age acceleration track disease severity, and do the different clocks agree with each other? Use the Amass API at api.amass.tech with my key in AMASS_API_KEY. Search BiomedCore for studies since 2023, keep only journals with journalQualityJufo 2 or higher, and for each paper pull out which clocks were measured and which direction each one moved. Lay it out as a matrix with one row per study and one column per clock, put the PMID on every row, and mark every cell where two studies disagree. Do not average the disagreements away. ``` The call behind the picture bash ``` curl "https://api.amass.tech/api/v1/cores/biomedcore/records\ ?query=epigenetic+clock+DNA+methylation+biological+age+acceleration\ &minJournalQualityJufo=2&minPublicationDate=2023-01-01&limit=6" \ -H "Authorization: Bearer amass_YOUR_KEY" ``` response · 200 OK · 6 records, JuFo ≥ 2 ``` TITLE JOURNAL DATE JUFO CITED Epigenetic age acceleration in idiopathic pulmonary… Am J Physiol Lung 2025-03-01 2 10 Accelerated Epigenetic Aging Is Associated with Fast… Ophthalmology 2025-05-01 3 9 Epigenetic age acceleration in peripheral blood corr… Brain 2025-08-01 3 5 Extracellular vesicles and epigenetic aging clocks i… J Gerontol A 2026-08-04 3 0 Low birthweight is associated with epigenetic age ac… Evol Med Public Hlth 2023-01-01 2 15 Epigenetic clock analysis of blood samples in drug-n… BMC Psychiatry 2023-01-17 2 13 ``` Starter data for this track [ #### CDC NHANES Population health and biomarker survey data, free to download. Open](https://wwwn.cdc.gov/nchs/nhanes/)[ #### NCBI GEO DataSets Public gene-expression studies, including most ageing signatures. Open](https://www.ncbi.nlm.nih.gov/gds) Also try GeneCore turns an ageing target into a defensible pick in one call. SIRT1, FOXO3, or whatever your hypothesis rests on comes back with its tractability, its genetic constraint, and its curated safety signals, each with the number behind it. That is the paragraph in your write-up that says why this gene and not another one. Track B2 ### Neural Systems Neural simulation, in-silico nervous systems, connectomics, and emergent behaviour. This is the track where a small, complete thing beats a large, half-wired thing: a hundred neurons that demonstrably learn one pattern is a finished entry. Brian2Allen Brain MapGeneCore How it is measured Pattern recall, response delay, network stability, or another metric you define, measured across a parameter sweep and against a baseline. localhost:8888/network #### Model card · the assumptions in your network SCN1A · Nav1.1 LOEUF 0.098decile 0 pLI 1.00loss intolerant Dependent lines 0 / 1,244not essential PDB entries 7DTD3D solved What it is Nav1.1, the pore-forming alpha subunit of a voltage-gated sodium channel. 2,009 aa, 229 kDa, four homologous domains of six transmembrane regions each. Kinetics you can set Phosphorylation at Ser-1516 by PKC slows inactivation and reduces peak sodium current. That is a knob in your model with a measurement behind it. What breaking it does Variants cause Dravet syndrome, generalized epilepsy with febrile seizures plus, and familial hemiplegic migraine 3. Your perturbation has a clinical phenotype attached. ENSG00000144285UniProt P354982q24.3 Your network, trained Weights shuffled The two panels are yours to produce. The card above them is the part you do not have to guess at, and it is the part a judge can check. Three places to start #### A hundred neurons that learn one pattern Train a small spiking network on a single pattern, then shuffle the synaptic weights and run it again. The comparison against the shuffled control is what turns a demo into a result. Brian2 · one evening #### Motifs under edge removal Take a small connectivity graph, delete a chosen set of edges, and show how the motif statistics move. Say which edges you chose and why before you show the plot. Allen Brain Map · half a day #### A sensory-to-motor loop that degrades Wire a simulated loop from sensing to movement, then raise the noise and the signal delay and plot where the response stops being stable. Name the breaking point. simulation · a few hours Paste this into Cursor to start prompt ``` Help me build a small spiking network and a real baseline. Goal: a 100-neuron spiking network in Brian2 that learns one input pattern, plus a control with the synaptic weights shuffled. Set it up so I can sweep one parameter and plot pattern recall against that sweep, with the control on the same axes. Give me a spike raster for both conditions. Then write the assumptions section for me: list every biological parameter I have hard-coded, and mark which ones I picked out of convenience rather than from a measurement. ``` The call behind the picture bash ``` curl "https://api.amass.tech/api/v1/cores/genecore/records\ ?query=SCN1A&include=protein&limit=1" \ -H "Authorization: Bearer amass_YOUR_KEY" ``` response · 200 OK · GeneCore SCN1A ``` symbol SCN1A ENSG00000144285 2q24.3 2,009 aa 229 kDa targetClass Ion channel › Voltage-gated ion channel › Voltage-gated sodium channel constraint LOEUF 0.098 pLI 1.00 (LOEUF decile 0) missense o/e 0.564 essentiality not essential, 0 dependent lines of 1,244 screened structure 3D: yes · PDB 7DTD · Pfam PF00520, PF24609, PF06512, PF11933 ptm Ser-1516 phosphorylation by PKC slows inactivation, lowers peak current disease Dravet syndrome · GEFSP2 · familial hemiplegic migraine 3 ``` Starter data for this track [ #### Allen Brain Map Atlases and connectivity data across species. Open](https://brain-map.org/)[ #### Brian2 tutorials The spiking-network simulator, from a standing start. Open](https://brian2.readthedocs.io/en/stable/resources/tutorials/index.html)[ #### fly-behavior-experiments A repository you can fork rather than start from an empty file. Open](https://github.com/The-Uploading-Lab/fly-behavior-experiments) Also try Amass does not simulate anything, and it is not meant to on this track. What it does is settle the assumptions argument. One GeneCore call tells you what is actually measured about the channel or receptor your model leans on, so the assumptions slide cites a record instead of a memory. Track B3 ### AI for Scientific Discovery Knowledge synthesis, scientific agents, experiment design, lab automation, and automated analysis. The trap on this track is building something that sounds right; the entries that land are the ones that show their sources and show where they failed. All six CoresCross-Core linksMCP How it is measured Citation support, reviewer agreement, false positives, or time saved, against a manual or simple baseline. A generated hypothesis is not a validated discovery, and saying so in the demo counts in your favour. localhost:3000/review-queue #### Claim check, awaiting a human 4 records · 2 flags Claim under test “Lecanemab is approved for early Alzheimer’s disease in the United States and the European Union on the same terms.” - ✓RegulatoryCoreLeqembi · FDAAMRC\_PxJ6… BLA · active · authorized 2023-01-06 · Eisai Inc · initiate at MCI or mild dementia - ⚑RegulatoryCoreLeqembi · EMAAMRC\_Nvtg… centralised · active · authorized 2025-04-15 · Eisai GmbH The EU indication is restricted to ApoE e4 non-carriers and heterozygotes. The US one is not. The claim of identical terms fails here. - ⚑TrialCoreClarity AD · NCT03887455AMTC\_F4vF… Phase 3 · Eisai · N = 1,906 · active, not recruiting · completion 2029-06-30 hasResults is false. The 18-month outcome everyone quotes is in the literature, not in this registry record. A tool that reads only the registry reports nothing here. - ✓TrialCoreStudy 201 · NCT01767311AMTC\_Jux8… Phase 2 · N = 856 · completed 2024-12-10 · results posted Verdict: partly supported. Two rows need a person.nothing ships until somebody signs off The value is not the answer. It is that every line carries the record it came from, and that the tool says where it is not sure. Three places to start #### An evidence graph for one question Answer a single research question with traceable papers, and make the tool state out loud which claims it cannot support. The refusals are the interesting output, not the answer. BiomedCore · one evening #### An experiment planner Propose the controls and a cost-aware design for one real lab question, then put it in front of somebody who runs that experiment and record what they changed. half a day #### A QC notebook that logs its decisions Find the outliers in a public assay table and write down every flagged row with the reason it was flagged. A reviewer should be able to overturn any one of them. a few hours Paste this into Cursor to start prompt ``` Build me a claim checker with a review step. Input: a claim about a drug, like "lecanemab is approved for early Alzheimer's disease in the US and the EU on the same terms". Use the Amass API at api.amass.tech with my key in AMASS_API_KEY. Search RegulatoryCore for every FDA and EMA authorization of that substance, and TrialCore for the trials behind it. For each record, say whether it supports or contradicts the claim, and quote the specific wording that decides it. Flag anything you are not sure about instead of resolving it, and refuse to output a verdict until a human has cleared the flags. Show me the failures as prominently as the passes. ``` The call behind the picture bash ``` curl "https://api.amass.tech/api/v1/cores/regulatorycore/records\ ?query=lecanemab&limit=4" \ -H "Authorization: Bearer amass_YOUR_KEY" ``` response · 200 OK · FDA + EMA, unified ``` NAME AGENCY STATUS AUTHORIZED HOLDER SUBSTANCE Leqembi FDA ACTIVE 2023-01-06 Eisai Inc Lecanemab-irmb Leqembi Iqlik FDA ACTIVE 2025-08-29 Eisai Inc Lecanemab-irmb Leqembi EMA ACTIVE 2025-04-15 Eisai GmbH Lecanemab Kisunla EMA ACTIVE 2025-09-24 Eli Lilly Nederland B.V. Donanemab # the EMA indications restrict to ApoE e4 non-carriers and heterozygotes. # the FDA one does not. that difference is the whole finding. ``` Starter data for this track [ #### OpenAlex API The open scholarly graph, for coverage across every field. Open](https://docs.openalex.org/)[ #### NCBI E-utilities Raw PubMed access, if you want to build the plumbing yourself. Open](https://www.ncbi.nlm.nih.gov/books/NBK25501/)[ #### Open Targets API Target-disease evidence, as a GraphQL endpoint. Open](https://platform-docs.opentargets.org/data-access/graphql-api) Also try Cross-Core links are the shortcut on this track. A gene resolves to the drugs that target it, those to the trials that ran them, those to the papers behind them and the FDA and EMA labels that cover them. Follow the reference fields one call at a time and every hop in your graph arrives with its own id. Treat an empty reference array as “none recorded”, not as “no evidence”. The offer ## Claim your team’s $500 in API credits One code per team, worth **$500**. Sign up with the email you registered for the hackathon with, then ask us for the code. 01 ### Sign up with your hackathon email Create an account at [platform.amass.tech](https://platform.amass.tech) using the same address you registered with on Luma. That is how we match you to the event. 02 ### Ask us for your team’s code Find the Amass table at the venue, ask in the event Discord, or email [hello@amass.tech](mailto:hello@amass.tech). Redeem it once per team, then check the balance with GET /v1/credits/api-credits. 03 ### Create a key and build Open [API Keys](https://platform.amass.tech/api-keys), click Create API Key — it starts with amass\_ and is shown once — and make your first call. What $500 buys Roughly **10,000 searches** or **50,000 record fetches**. Search costs $0.05 per 20 results; get-by-id and lookup are $0.01 each. That is far more than a team gets through in 24 hours, so query freely and cache what you re-run. Start here ## Four ways in, one key All four reach the same data with the same key. If you do not know which one you want, start with the assistant and let it write the first call with you. Start with this ### The Amass build assistant A chat grounded in the Amass documentation, built to help you write against the API. Describe what you are trying to do, such as finding every completed trial of an amyloid antibody with posted results and the papers behind them. It answers with the endpoint, the filters, and the field names, so you are not guessing at parameter names from a schema you have not read. [Open the assistant →](https://platform.amass.tech/assistant) [ ### The platform Your account, credits, and API keys, plus the full documentation, the interactive API reference, and the app gallery. Everything the assistant reads from lives here. platform.amass.tech](https://platform.amass.tech)[ ### The Amass app The product itself. Ask a research question and get an answer with the records attached. Use it on Saturday morning to see what the data can answer before you write a line of code. preview.amass.tech](https://preview.amass.tech)[ ### MCP Connect Amass to Claude, Cursor, or Codex and query all six Cores in natural language while you build. It is also the fastest route to a judge-friendly demo with nothing to deploy. Connect the MCP](https://amass.tech/mcp) The data ## Six Cores, one API Same base URL, same auth header, same error shape across all of them. A paper, a trial, a molecule, a gene, an authorization, and a patent can resolve to the same entity, so you follow one thread instead of joining three exports by hand. ### BiomedCore 40M+ PubMed & PMC papers indexed /cores/biomedcore/records ### TrialCore 1.2M+ Clinical trials indexed /cores/trialcore/records ### DrugCore 22K+ ChEMBL drugs & molecules indexed /cores/drugcore/records ### RegulatoryCore FDA + EMA FDA & EMA documents indexed /cores/regulatorycore/records ### GeneCore 43K+ Human genes indexed /cores/genecore/records ### PatentCore 16M+ Life-science patents indexed Preview /cores/patentcore/records Apps to fork ## You do not have to start from an empty file Every app in the gallery runs on the same API, the same key, and the same cited records as the tracks above. Several were built with [Lovable](https://amass.tech/life-science-api) in an afternoon, so there is a route through this weekend that involves no local setup at all. Fork one, point it at your key, and change the question it asks. [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. Each one is a working answer to “what does a small app on this data look like?” The 24 hours ## How to spend the weekend The times are the organisers’. The line next to each one is the thing we would tell a team standing in front of us at that moment. 1. Fri 16:00Day 0 crash course Optional, and it runs to 21:15. If you have never shipped anything with a coding agent before, this is the cheapest five hours of the weekend. 2. Sat 12:30Opening, rules, briefing Read your track’s Measure line before you commit to an idea. It is the rubric, and it is two sentences long. 3. Sat 13:00Team-finding, then building Spend the first hour narrowing rather than coding. One question, one population, one outcome. Teams are one to four people and solo entries are welcome. 4. Sat 19:00Show and Tell I Bring the ugly version. Criticism is far cheaper now than it is at 13:00 tomorrow, and this is the last point where changing direction is free. 5. Sun 09:00Show and Tell II Freeze the scope here. Anything not working by now is a sentence in the demo, not a feature in the build. 6. Sun 11:00Demo video Record it while the build still works. A short demo of a working thing beats a long one of an almost-working thing, every time. 7. Sun 13:00Submission, then judging Hand in the video and the repository. Check the event page for the exact submission form. Playbook ## Six things worth knowing before 13:00 on Saturday These are what separate an entry that lands from one that spends the weekend fighting its own plumbing. 01 ### Narrow until it feels too small. Twenty-four hours is the real constraint, and every track’s brief opens by asking for one narrow, testable question. A finished small thing wins against an unfinished large one, and the judges can tell the difference from the back of the room. 02 ### Build the baseline first. Every Measure line in Block B asks for a comparator. Shuffled weights, a random classifier, the manual version of the workflow. The baseline takes twenty minutes and it is what turns your number into a result. 03 ### Show where it fails. Two of the three track briefs ask for failures explicitly. A demo that names its own failure mode reads as finished work. A demo that hides it reads as a demo that has not been tested. 04 ### Cite it or drop it. Every Amass record carries a stable id and a route back to its source, so there are no invented PMIDs and no invented NCT numbers. That is the whole reason to query the data rather than ask a model from memory. 05 ### Let the build assistant write the calls. Describe what you want in plain language and it answers with the endpoint, the filters, and the field names, straight from the documentation. That removes the part of the weekend where you guess at parameter names from a schema nobody has read. 06 ### Read the envelope, respect the limit. Results arrive under data and failures arrive under error, so check for an error before you parse. The limit is 60 requests per 60 seconds; on a 429, read Retry-After and back off. There is no paging, so narrow with filters instead. Prototype in Claude ## Sketch it in a chat before you write any app code. Connect the Amass MCP in Claude, Cursor, ChatGPT, or Codex and query all six Cores in natural language. Explore the data, find the filters that work, and only then drop the same queries into your build. On Sunday it doubles as the demo: the answers come back cited in the chat, with nothing to deploy. In Claude Code, install the Amass skill and let the model wire up your data layer for you. That is a short path from “what is even in here?” to a working prototype, which is the right question to answer on Saturday afternoon rather than Sunday morning. [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) ## Pick one, and go build it Claim your team’s $500, choose a track, and let the API do the reading. Narrow it until it feels too small, build the baseline, and show where it breaks. We would like to see it on Sunday. [Claim your team’s $500 →](https://platform.amass.tech)[Build assistant](https://platform.amass.tech/assistant)[Try the app](https://preview.amass.tech)[Connect the MCP](https://amass.tech/mcp) One code per team · ask at the Amass table, in the event Discord, or at [hello@amass.tech](mailto:hello@amass.tech)