# The Hackathon Issue > Source: > Published: 2026-07-30 09:54:43+00:00 *Seven problems worth 48 hours of your life. With Python.* July 2026 | Abhinav Jain Most hackathon articles list problems the way a waiter recites specials. Fast. Forgettable. You nod. You order something safe. This is not that. I am going to give you seven problems that I would personally lose sleep over. Each one sits at the exact intersection of what Python does well in 2026, what AI tooling now makes genuinely possible, and what the world is actually breaking under. Not themes. Problems. The code is starter code, not finished code. The thinking is the point. *A hackathon is not a product sprint. It is a proof-of-belief. The best submissions do not win because they work. They win because they show the judges that someone understood the problem one layer below where everyone else was looking.* That one-layer-below thing. That is the Breath Hacker operating principle. I have used it in cloud kitchens, in programmatic advertising pipelines, and in narrative strategy for founders who are tired of surface-level positioning. It applies here too. Read all seven. Pick one that makes you uncomfortable. That is your problem. 01 **Context Management / LLM Agents** **The Memory Tax** Every LLM conversation starts over. We have accepted this as if it were a law of physics. It is not. It is a design choice that nobody has solved well yet. The problem: users of AI tools rebuild context every single session. They paste the same background, repeat the same constraints, re-explain the same preferences. A therapist who forgot your last session every week would lose their license. We just call it a context window and move on. Your hackathon project: build a lightweight personal memory layer that sits between the user and any LLM. It captures structured context automatically from conversations, lets the user inspect and edit what got remembered, and injects the right subset into each new session without bloating the prompt. *The question is not ‘can the AI remember?’ The question is ‘what is worth remembering, and who decides?’* Why this is hard: relevance. Raw retrieval by similarity gives you stuff that is adjacent, not stuff that is useful. The real work is building a scoring function that prefers recency, surprise (things the user would not have stated explicitly), and stakes (decisions with consequences). Python starter path: # pip install anthropic chromadb sentence-transformers import json from anthropic import Anthropic import chromadb from sentence_transformers import SentenceTransformer client = Anthropic() chroma = chromadb.Client() collection = chroma.get_or_create_collection(‘user_memory’) embedder = SentenceTransformer(‘all-MiniLM-L6-v2’) def store_memory(text: str, metadata: dict): vec = embedder.encode([text])[0].tolist() collection.add( documents=[text], embeddings=[vec], metadatas=[metadata], ids=[metadata.get(‘id’, text[:40])] ) def recall(query: str, n: int = 5) -> list[str]: vec = embedder.encode([query])[0].tolist() results = collection.query(query_embeddings=[vec], n_results=n) return results[‘documents’][0] def chat_with_memory(user_message: str, history: list) -> str: memories = recall(user_message) memory_block = ‘\n’.join(memories) system = f’Relevant context from past sessions:\n{memory_block}’ history.append({‘role’: ‘user’, ‘content’: user_message}) response = client.messages.create( model=’claude-sonnet-4–6', max_tokens=1024, system=system, messages=history ) reply = response.content[0].text history.append({‘role’: ‘assistant’, ‘content’: reply}) # Auto-extract and store new memories here return reply The extraction step is where most teams stop too early. Build a second LLM call that reads the exchange and asks: ‘What is worth remembering from this?’ Structure the output as JSON with keys: fact, stakes, expiry. That expiry field alone puts you in the top 10% of submissions. 02 **Emotional Intelligence / Email AI** **The Grief in the Inbox** Your email client does not know that Tuesday’s thread from your father arrived on the same day you got a difficult diagnosis. It treats all messages as equal. Equally urgent. Equally loud. That is a user experience failure disguised as a feature gap. The problem: emotional context is invisible to productivity tools. We have AI that can summarize, schedule, draft, and triage. None of it knows when you are fragile. None of it knows when a particular sender matters more than the subject line suggests. Your project: an emotionally-aware email prioritization layer. Not sentiment analysis on the emails themselves. Tone profiling of the user, updated in real time. When the system detects elevated stress in the user’s own outgoing drafts, it quiets the inbox. When a flagged sender appears (someone the user has marked as emotionally significant), it surfaces them regardless of urgency score. *We have built inboxes that respond to importance. Nobody has built one that responds to the human reading it.* This will feel soft to some judges. Those judges are wrong. The market for tools that reduce cognitive and emotional load is enormous, and it is 2026 now. The data exists. The tooling exists. The missing piece is someone who believed the problem was real enough to solve it. # pip install anthropic google-auth google-auth-oauthlib google-api-python-client import re from anthropic import Anthropic from googleapiclient.discovery import build client = Anthropic() def score_user_tone(draft_text: str) -> dict: resp = client.messages.create( model=’claude-sonnet-4–6', max_tokens=256, system=’You are a tone analyst. Return JSON only.’, messages=[{ ‘role’: ‘user’, ‘content’: f’Analyze this email draft for stress markers. Return: {{“stress”: 0–10, “fatigue”: 0–10, “urgency_self_reported”: 0–10}}\n\n{draft_text}’ }] ) import json return json.loads(resp.content[0].text) def prioritize_emails(emails: list, user_stress_score: int, vip_senders: list) -> list: # If user stress > 7, surface only VIP senders and direct questions if user_stress_score > 7: return [e for e in emails if e[‘from’] in vip_senders or ‘?’ in e[‘snippet’]] return sorted(emails, key=lambda e: e.get(‘importance’, 0), reverse=True) The VIP sender list should be learnable, not just manually set. Build a simple frequency-plus-response-time model: people you reply to quickly, consistently, across months. Your behavior already knows who matters to you. Extract it. 03 **AI Safety / RAG / Fact Checking** **The Hallucination Firewall** Here is a problem every organization with an AI chatbot has right now and almost nobody has solved cleanly: the model confidently says things that are not in the documents it was given. RAG reduces this. It does not eliminate it. The model still interpolates. Still fills gaps. Still sounds certain when it should say ‘this is not in my source material.’ Your project: a post-generation verifier that checks LLM outputs against a document corpus and flags or rewrites claims that lack grounding. Not a vibe check. A citation engine. Every sentence in the output gets a confidence score tied to actual source coverage. *Confident is not the same as correct. We built tools that sound confident. We have not built tools that know the difference.* This is relevant in legal, medical, financial, and government contexts, all of which are actively buying AI tooling right now. A clean demo of this in a 48-hour window is genuinely commercially interesting. # pip install anthropic chromadb sentence-transformers from anthropic import Anthropic import chromadb from sentence_transformers import SentenceTransformer import json client = Anthropic() chroma = chromadb.Client() collection = chroma.get_or_create_collection(‘source_docs’) embedder = SentenceTransformer(‘all-MiniLM-L6-v2’) def verify_claim(claim: str, threshold: float = 0.72) -> dict: vec = embedder.encode([claim])[0].tolist() results = collection.query(query_embeddings=[vec], n_results=3) top_score = max(results[‘distances’][0]) if results[‘distances’] else 0 top_sources = results[‘documents’][0] return { ‘claim’: claim, ‘grounded’: top_score >= threshold, ‘confidence’: round(top_score, 3), ‘supporting_sources’: top_sources } def verify_response(llm_output: str) -> list[dict]: # Split output into sentences for per-claim checking resp = client.messages.create( model=’claude-sonnet-4–6', max_tokens=512, system=’Return JSON only. No preamble.’, messages=[{ ‘role’: ‘user’, ‘content’: f’Extract all factual claims from this text as a JSON array of strings:\n\n{llm_output}’ }] ) claims = json.loads(resp.content[0].text) return [verify_claim(c) for c in claims] Two things that make a hackathon demo pop here: a live UI that shows green / amber / red confidence beside each sentence, and a ‘rewrite with caveats’ mode that automatically softens ungrounded claims. Build both. The visual is the argument. 04 **Predictive Analytics / B2B SaaS** **The Silent Churn Signal** Every B2B SaaS company watches their dashboard and calls it retention management. The dashboard shows what already happened. Churn is a decision that gets made three weeks before the cancellation notice arrives. The problem: the signals that predict churn are scattered across product analytics, support tickets, email open rates, and NPS scores. Nobody has joined them. The data team is busy. The PM is looking at activation metrics. The customer success rep is working from a spreadsheet. Your project: a multi-signal churn predictor that ingests behavioral data from multiple sources, runs an LLM over support ticket language to detect frustration drift, and outputs a ranked list of at-risk accounts with the specific signal that fired. Not a score. A story. ‘This account stopped using the export feature two weeks after they submitted a ticket about it being broken. Nobody replied to that ticket for 6 days.’ *A number tells you who is churning. A story tells you why. Only the story is actionable.* # pip install anthropic pandas scikit-learn from anthropic import Anthropic import pandas as pd from sklearn.ensemble import IsolationForest import json client = Anthropic() def detect_frustration_in_ticket(ticket_text: str) -> dict: resp = client.messages.create( model=’claude-sonnet-4–6', max_tokens=256, system=’Analyze support tickets. Return JSON only.’, messages=[{ ‘role’: ‘user’, ‘content’: f’Score this ticket. Return {{“frustration”: 0–10, “unresolved_expectation”: true/false, “churn_signal_phrases”: [list]}}\n\n{ticket_text}’ }] ) return json.loads(resp.content[0].text) def flag_anomalous_accounts(usage_df: pd.DataFrame) -> pd.DataFrame: # usage_df: one row per account, columns = feature usage counts model = IsolationForest(contamination=0.1, random_state=42) usage_df[‘anomaly_score’] = model.fit_predict(usage_df.select_dtypes(‘number’)) return usage_df[usage_df[‘anomaly_score’] == -1] def generate_churn_story(account_id: str, signals: dict) -> str: resp = client.messages.create( model=’claude-sonnet-4–6', max_tokens=300, messages=[{ ‘role’: ‘user’, ‘content’: f’Write a 2-sentence churn risk summary for account {account_id}. Data: {json.dumps(signals)}. Be specific. No fluff.’ }] ) return resp.content[0].text The Isolation Forest is doing heavy lifting here to find accounts whose behavior changed, not accounts that are already low. That is the key insight. You are looking for the inflection point, not the floor. 05 **Financial Inclusion / NLP** **The Language of the Unbanked** There are still a few hundred million people across South and Southeast Asia, Sub-Saharan Africa, and parts of Latin America who interact with financial systems primarily through voice, through local-language text, or not at all. Most fintech AI is built on English. Most fintech AI assumes a certain level of financial vocabulary. Most fintech AI assumes a smartphone with a solid data connection. Your project: a voice-first, low-literacy financial assistant that explains loan terms, savings products, and transaction histories in simple local-language sentences. No jargon. No charts. Audio output. Works on 2G. *The people who most need financial clarity are the ones least served by the interfaces we build financial clarity into.* I have spent time on this problem through Signals and Society conversations across the Gulf and South Asia. The gap is not data. The gap is translation, two kinds: linguistic and conceptual. ‘APR’ is not just an English word. It is a concept that requires three prior concepts to be meaningful. # pip install anthropic gtts requests from anthropic import Anthropic from gtts import gTTS import io, base64 client = Anthropic() SIMPLIFICATION_PROMPT = ‘’’ You are a financial assistant for people with limited formal education. Rules: - Use words a 10-year-old would know - No acronyms ever (not APR, EMI, LTV, nothing) - One idea per sentence - Use local metaphors when helpful (grain storage, market day, harvest cycle) - Max 80 words - Respond in the same language the user wrote in ‘’’ def explain_financial_term(term_or_document: str, language: str = ‘hi’) -> dict: resp = client.messages.create( model=’claude-sonnet-4–6', max_tokens=200, system=SIMPLIFICATION_PROMPT, messages=[{ ‘role’: ‘user’, ‘content’: f’Explain this in {language}: {term_or_document}’ }] ) text = resp.content[0].text tts = gTTS(text=text, lang=language) buf = io.BytesIO() tts.write_to_fp(buf) audio_b64 = base64.b64encode(buf.getvalue()).decode() return {‘text’: text, ‘audio_base64’: audio_b64, ‘lang’: language} The winning extension here is a document ingestion layer. Upload any loan agreement PDF. The system extracts the five most important facts (interest rate, penalty clause, tenure, total repayable, hidden fees) and reads them out in the user’s language. That is a product. Right now. 06 **Agent Orchestration / Productivity** **The Attention Broker** We have built a world where everything wants 3% of your attention. The net effect is not productivity. It is a low-grade cognitive drain that most knowledge workers have normalized as the job. The problem: there is no system that knows what you are actually trying to think about right now, and deflects everything else. Your calendar knows your appointments. Your email client knows your messages. Nobody has joined them and made a decision on your behalf: is this worth interrupting the thing you told me you were doing? Your project: an attention-aware orchestration agent that monitors a user’s declared focus state and routes incoming information (emails, Slack messages, calendar invites, news alerts) through a relevance gate before it reaches them. The gate is not rules-based. It is LLM-judgment-based, informed by what the user said they were working on. *Every notification is a bet that this is more important than whatever you were thinking about. Almost none of them win that bet.* # pip install anthropic slack-sdk google-auth-oauthlib from anthropic import Anthropic import json, datetime client = Anthropic() class AttentionGate: def __init__(self, focus_task: str, focus_until: datetime.datetime): self.focus_task = focus_task self.focus_until = focus_until self.held_queue = [] def evaluate(self, signal: dict) -> dict: if datetime.datetime.now() > self.focus_until: return {‘action’: ‘deliver’, ‘reason’: ‘focus session ended’} resp = client.messages.create( model=’claude-sonnet-4–6', max_tokens=200, system=’You are an attention guardian. Return JSON only.’, messages=[{ ‘role’: ‘user’, ‘content’: ( f’User is focused on: {self.focus_task}\n’ f’Incoming signal: {json.dumps(signal)}\n’ ‘Should this interrupt the user now? Return: ‘ ‘{“action”: “deliver” or “hold” or “discard”, “reason”: “…”, “urgency”: 0–10}’ ) }] ) return json.loads(resp.content[0].text) def hold(self, signal: dict): self.held_queue.append(signal) def flush_summary(self) -> str: if not self.held_queue: return ‘Nothing held during your session.’ resp = client.messages.create( model=’claude-sonnet-4–6', max_tokens=400, messages=[{ ‘role’: ‘user’, ‘content’: f’Summarize these held signals in order of priority. Be brief.\n{json.dumps(self.held_queue)}’ }] ) return resp.content[0].text The flush summary at session end is the UX moment that will win you the demo. Show a judge 40 minutes of held notifications collapsed into 4 bullet points, ranked by what actually needed their attention. That experience is viscerally satisfying in a way that a features list never is. 07 **Misinformation / AI Journalism Tools** **The Evidence Audit** A rumor travels at the speed of a share. A correction travels at the speed of a newsletter nobody opens. The problem in 2026 is not shortage of fact-checking. It is that fact-checking is reactive, slow, and invisible to the people who most needed it before they shared the thing. By the time the correction exists, the damage is compounding. Your project: a browser extension plus backend that intercepts a share action, runs the claim through a real-time evidence audit (web search, structured knowledge base lookup, credibility scoring of cited sources), and gives the user a two-second verdict before the post goes out. Green, amber, red. With a one-sentence reason. *We do not need more fact-checkers. We need fact-checking at the moment of the decision to share, not three days later.* The web search tool in the Anthropic API is your best friend here. As of mid-2026 it is available natively, which means you are not stitching together three services to get fresh information into your LLM context. # pip install anthropic from anthropic import Anthropic import json client = Anthropic() def audit_claim(claim: str) -> dict: resp = client.messages.create( model=’claude-sonnet-4–6', max_tokens=512, tools=[{‘type’: ‘web_search_20250305’, ‘name’: ‘web_search’}], system=( ‘You are a fact-checker. Always search before concluding. ‘ ‘After searching, return JSON only: ‘ ‘{“verdict”: “true” | “false” | “misleading” | “unverifiable”, ‘ ‘“confidence”: 0–10, ‘ ‘“one_sentence_reason”: “…”, ‘ ‘“sources”: [list of URLs]}’ ), messages=[{ ‘role’: ‘user’, ‘content’: f’Fact-check this claim: {claim}’ }] ) # Extract the final text block after tool use for block in reversed(resp.content): if block.type == ‘text’: return json.loads(block.text) return {‘verdict’: ‘unverifiable’, ‘confidence’: 0} # Example # result = audit_claim(‘The UAE has banned all social media platforms in 2026’) # print(result) Build the browser extension in vanilla JavaScript. It does not need React. It needs to intercept the Twitter / X share button, LinkedIn post action, and WhatsApp forward. One input. One API call. One verdict. That is the whole product. **Before You Pick** Seven problems. One lens: where is the world broken in a way that Python plus a good LLM could visibly improve in 48 hours? Notice I did not give you a single CRUD app. I did not give you a chatbot wrapper. I did not give you anything that takes a dataset and draws a bar chart. Every problem here has a human cost at its center. Memory Tax: the cognitive exhaustion of rebuilding context. Grief in the Inbox: emotional blindness in productivity tools. Hallucination Firewall: the trust deficit in AI-assisted work. Silent Churn Signal: the gap between what data shows and what action requires. Language of the Unbanked: exclusion dressed up as a product gap. Attention Broker: the slow hemorrhage of focus. Evidence Audit: the speed asymmetry between misinformation and correction. Pick the one where you felt something shift, not just ‘oh that is interesting.’ That shift is your signal. That is you recognizing a problem you are actually equipped to care about. The code blocks are not answers. They are starting points. Meaning the folder structure, the calling convention, the schema design, the error handling, the demo flow, all of that is still yours. Good. That is where hackathons are actually won. One last thing. The best submission I ever saw at a hackathon was not the best code. It was the clearest explanation of why the problem mattered to the person who built it. That clarity is the thing judges cannot teach you and cannot manufacture for you. You already know which one of these you would lose sleep over. Build that one. In 2026, the accessible stack (Python + Claude API + ChromaDB + a bit of patience) is good enough to build genuinely useful tools in 48 hours. The constraint is no longer technical. It is clarity of problem. Every problem above has a real human on the other end who would thank you for a working version. That is a better design brief than any hackathon prompt sheet you will ever be handed. Pick the one that keeps you up. Shipping it is the easy part. A hackathon is not a marathon of cleverness. It is a concentrated argument that a specific pain is real and solvable. The teams that win are the ones who understood the pain first and the code second. The code is just how you show your work. [The Hackathon Issue](https://blog.stackademic.com/the-hackathon-issue-ea3dc9c03106) was originally published in [Stackademic](https://blog.stackademic.com) on Medium, where people are continuing the conversation by highlighting and responding to this story.