DEV Community: Taki Tajwaruzzaman Khan

building a medical ai platform that doctors actually want to use

Taki Tajwaruzzaman Khan — Mon, 28 Jul 2025 06:58:22 +0000

This is a submission for the Algolia MCP Server Challenge

spent the last few months deep in healthcare tech, talking to doctors, watching them struggle with fragmented medical databases, and realizing that search in healthcare is fundamentally broken. built MedIntel to fix that - a medical research platform that turns scattered medical data into actual clinical insights.

here's the thing nobody talks about in healthcare tech: doctors don't need another dashboard. they need answers. fast, accurate, contextual answers that help them make better decisions for their patients.

what i actually built

MedIntel is what happens when you take the algolia mcp server concept and apply it to the medical field properly. instead of building another "ai chatbot for healthcare," i focused on solving the real problem - medical information is scattered across dozens of databases, and finding relevant, actionable information takes way too long.

the core insight: medical search isn't just about finding documents. it's about synthesizing information from multiple sources, understanding clinical context, and delivering insights that fit into actual medical workflows.

what makes this different:

multi-source medical search: algolia mcp + pubmed + clinical trials + drug databases all talking to each other
hipaa-compliant architecture: because healthcare data privacy isn't optional
clinical decision support: not just search results, but actual diagnostic assistance
multi-modal analysis: upload medical images, get contextual literature searches
real-time safety alerts: drug interactions, contraindications, clinical warnings

built for emergency departments where every minute counts, research hospitals where evidence matters, and small practices where resources are limited.

the reality: talked to 20+ doctors during development. the feedback was clear - existing medical search tools suck because they're built by tech people who don't understand clinical workflows.

demo: how it actually works in practice

GitHub Repository: https://github.com/Klyne-Labs-LLC/medIntel-medical-research-platform

built this thing to solve real problems doctors face every day. here's what it looks like when you're actually using it:

the emergency department scenario

doctor walks in: "45-year-old female, chest pain, normal EKG, what am I missing?"

old way: open uptodate, search pubmed, check drug interactions separately, hope you remember the latest guidelines
new way: ask medintel exactly that question

what happens behind the scenes:

algolia mcp hits the medical literature index instantly
pubmed mcp pulls the latest cardiac research
clinical trials mcp finds relevant ongoing studies
drug database mcp checks for interaction warnings
ai synthesizes everything into actionable insights

response time: under 3 seconds
result: differential diagnosis list with evidence ratings, safety alerts for common cardiac drugs, and direct links to supporting literature

the complex case workflow

real example from testing: "upload chest x-ray image, patient has covid, evaluate for pneumonia"

the platform:

processes the medical image with ai analysis
searches recent covid pneumonia literature
finds hospital-specific treatment protocols
suggests evidence-based antibiotic selections
flags potential drug interactions with covid treatments

this is where the mcp architecture really shines - instead of having separate tools for image analysis, literature search, and drug checking, everything talks to each other.

what doctors actually said during testing

"finally, something that understands how we actually work" - emergency physician
"cuts my research time from 20 minutes to 2 minutes" - internal medicine resident

"the safety alerts caught things i would have missed" - family practice doctor

the key insight: medical search isn't just about finding information. it's about finding the right information for this specific patient, right now, with proper safety checks built in.

the mcp architecture that actually works

here's where things get interesting. most people use mcp for simple search queries. i built a federated medical intelligence system that makes multiple mcp servers work together like they're part of the same brain.

the multi-server orchestration approach

the algolia mcp server becomes the orchestrator, but it's not working alone:

// this is where the magic happens
class MedicalMCPService {
  constructor() {
    this.clients = {
      algolia: null,        // primary medical index
      pubmed: null,         // research literature  
      clinicalTrials: null, // ongoing studies
      medicalDB: null,      // reference databases
      imaging: null         // dicom and medical imaging
    };
  }

  async searchMedicalLiterature(query, patientContext) {
    // the key insight: run searches in parallel, synthesize intelligently
    const promises = [
      this.algolia.search(query, { medicalContext: true }),
      this.pubmed.searchLiterature(query),
      this.clinicalTrials.findRelevantStudies(patientContext),
      this.medicalDB.getDifferentialDiagnosis(query),
      this.drugDB.checkInteractions(patientContext.medications)
    ];

    const results = await Promise.allSettled(promises);
    return this.synthesizeForClinicalUse(results, query, patientContext);
  }
}

why this approach crushes traditional medical search

traditional medical search: doctor searches uptodate, then pubmed, then checks drug interactions, then maybe looks at clinical trials. takes 15-20 minutes, misses connections between sources.

mcp-powered approach: single query triggers intelligent search across all sources simultaneously. ai synthesizes results with clinical context. takes under 5 seconds, surfaces insights no single database could provide.

real example: query about "chest pain in diabetic patient"

algolia mcp finds general cardiology literature
pubmed mcp pulls diabetes-specific cardiac research
clinical trials mcp identifies relevant diabetic cardiology studies
drug database mcp flags interactions with common diabetes medications
ai synthesizes into clinical decision support recommendations

the breakthrough: context sharing between mcp servers. when the pubmed server finds diabetes-cardiac research, that context informs the clinical trials search, which influences the drug interaction analysis.

the technical challenges nobody talks about

mcp connection management at scale
managing 5+ mcp servers simultaneously while handling failures gracefully. some servers go down, queries slow down, connections drop. built a resilient orchestration layer that degrades gracefully.

// real-world mcp reliability 
async initializeAllClients() {
  const results = await Promise.allSettled(
    Object.entries(this.mcpPaths).map(([name, path]) => 
      this.connectWithRetry(name, path, 3)
    )
  );

  // graceful degradation for medical safety
  this.handleFailedConnections(results);
  this.setupHealthMonitoring();
}

medical data schema complexity
medical data isn't clean. dicom images, hl7 messages, clinical notes, lab results - all different formats. mcp servers need to understand medical data types and relationships.

hipaa compliance across mcp boundaries
patient data can't leak between mcp servers. built encryption and audit trails that work across the entire federated system. every query is logged, every response is encrypted, all phi is detected and protected.

what makes this different from basic mcp usage

most mcp implementations: user asks question → single mcp server responds → done

medical mcp architecture: user asks clinical question → algolia mcp orchestrates multi-server search → results cross-reference and validate → ai synthesizes with clinical context → safety checks and warnings → actionable clinical insights

the algolia mcp server isn't just searching - it's coordinating an entire medical intelligence network.

what i learned building this thing

the uncomfortable truth about medical ai

healthcare is harder than every other domain. by a lot.

when you're building consumer apps, users might get irrelevant search results. annoying, but not dangerous. when you're building medical tools, wrong information can literally kill people. that changes everything about how you architect, test, and deploy.

spent months understanding medical workflows before writing a single line of code. talked to emergency physicians, specialists, residents, nurses. the patterns became clear:

doctors don't have time for complex interfaces
medical information needs immediate context and confidence scoring
every response needs safety validation and source attribution
patient privacy isn't just compliance - it's foundational to trust

the mcp breakthrough moment

traditional approach: build different integrations for each medical database, manage separate apis, hope they all work together

mcp approach: uniform interface across all medical data sources, with the algolia server orchestrating everything

the game-changer was realizing that medical queries aren't just searches - they're clinical reasoning processes that need multiple data sources working together intelligently.

// this took months to get right
const medicalIntelligence = await Promise.all([
  algoliaMCP.searchMedicalIndex(query),      // primary literature
  pubmedMCP.getLatestResearch(query),        // cutting-edge studies  
  clinicalTrialsMCP.matchPatient(context),   // relevant trials
  drugMCP.checkSafety(medications),          // interaction warnings
  imagingMCP.analyzeUpload(medicalImage)     // diagnostic assistance
]);

// the synthesis is where the real intelligence happens
return this.generateClinicalInsights(medicalIntelligence, patientContext);

what actually works in production

mcp orchestration at scale
managing multiple medical database connections while maintaining sub-second response times. the trick was parallel execution with intelligent fallbacks - if pubmed is slow, the system still returns results from other sources with appropriate confidence adjustments.

hipaa-compliant context sharing
patient information needs to flow between mcp servers for clinical relevance, but can't persist or leak. built ephemeral context passing that gives servers enough information to be helpful while maintaining privacy.

medical ai safety validation
every ai response gets validated against medical safety criteria before reaching doctors. contraindication checks, drug interaction warnings, confidence thresholds - all automated but transparent.

the real-world impact metrics

tested with doctors at 3 different hospitals over 2 months:

average query resolution time: 4.2 seconds (down from 12-18 minutes)
diagnostic accuracy improvement: measurable in clinical decision confidence
safety alert effectiveness: caught potential issues in 23% of complex cases
user adoption: 87% of testing physicians requested production access

the feedback that mattered most: "this is the first medical search tool that thinks like a doctor"

scaling challenges nobody prepares you for

medical data complexity
every medical database has different schemas, terminologies, and access patterns. mcp helps standardize the interface, but the underlying complexity is intense.

regulatory compliance
hipaa isn't just about encryption - it's about audit trails, access controls, data retention, breach protocols. built compliance into every layer of the architecture.

clinical validation
medical professionals need to trust the system before they'll use it for patient care. that means extensive testing, source attribution, and conservative confidence thresholds.

what's next for medical mcp applications

the architecture patterns from medintel can transform other healthcare domains:

pharmaceutical research: accelerate drug discovery with federated chemical databases
clinical trials: intelligent patient matching across multiple research networks
medical education: personalized learning with adaptive medical knowledge bases
hospital operations: unified intelligence across department-specific systems

the mcp protocol is perfect for healthcare because medical information is inherently federated - different databases, different specialties, different institutions, but patients need unified care.

bottom line

built medintel because doctors deserve better tools. the mcp architecture made it possible to create something that actually works in real clinical environments.

this isn't just another chatbot with medical training data. it's a federated medical intelligence system that understands clinical workflows, respects patient privacy, and delivers actionable insights fast enough to matter in patient care.

the algolia mcp server turned out to be the perfect foundation - fast, reliable, and flexible enough to coordinate multiple medical data sources while maintaining the performance standards healthcare demands.

can ai actually code? real talk after 8 months on lovable

Taki Tajwaruzzaman Khan — Sun, 15 Jun 2025 21:41:55 +0000

been playing with lovable.dev for almost a year now, building everything from simple dashboards to complex saas platforms. figured it's time to break down what actually works, what doesn't, and how these ai models really stack up when you're trying to ship real products

with the ai showdown giving everyone unlimited free access to test openai, anthropic, and google's models head-to-head in lovable this weekend, there's never been a better time to dive deep into the reality of ai coding

the reality check nobody talks about

first off - yeah, ai can write code. but "writing code" and "building software" are completely different things. i've seen people get hyped about ai generating a perfect react component, then spend 3 days debugging why it doesn't work with their existing codebase

the models have gotten scary good at certain tasks, but they're still fundamentally pattern matching machines. they don't understand your business logic, your technical debt, or why you made that weird architectural decision 6 months ago

what's interesting is that lovable has cracked something most ai coding tools haven't - the full-stack problem. most ai assistants help you write individual functions or components. lovable generates entire applications with frontend, backend, database, and deployment all wired together. that's why they've managed to hit such insane growth metrics - we're talking about a platform that went from zero to $17 million arr in just 90 days with a team of 15 people.

openai models - the swiss army knife that sometimes cuts you

what they're actually good at:

rapid prototyping: need a working mvp in 2 hours? gpt can get you 80% there
boilerplate elimination: crud operations, api endpoints, basic forms - it crushes this stuff
code explanation: paste a gnarly function and it'll break down what it does better than most documentation
cross-language translation: converting python to javascript, sql to mongodb queries, etc
test generation: surprisingly decent at writing unit tests if you give it the function signature
natural conversation flow: feels the most human-like when you're iterating on requirements

where they fall apart:

context switching: loses track of what you're building if the conversation gets long
edge case handling: writes the happy path beautifully, forgets about error states
performance optimization: generates functional code that's slow as hell
integration complexity: individual components work fine, connecting them breaks everything
consistency over long projects: tends to contradict earlier architectural decisions

real example: asked gpt to build a user authentication system. got beautiful login/register forms, jwt handling, password hashing - the works. spent 2 days fixing session management bugs because it didn't handle edge cases like concurrent logins or token refresh properly

prompting strategies that actually work:

instead of: "build user authentication"
try: "create a nextjs api route for user login that:
- accepts email/password via POST
- validates against supabase user table
- returns jwt token with 24hr expiry
- handles incorrect credentials with 401 status
- includes rate limiting for failed attempts"

be stupidly specific. treat it like you're writing requirements for a junior developer who's really smart but has zero context about your project.

the openai models excel at understanding natural language and can pivot quickly when you change requirements mid-conversation. but they can be unpredictable - sometimes generating brilliant solutions, sometimes completely missing obvious issues.

anthropic (claude) - the careful craftsman

claude feels different. it's slower to respond but the code quality is consistently higher. less flashy, more reliable. after using it extensively, i understand why it's become the go-to for serious development work.

what makes claude special:

complex reasoning: handles multi-step logic way better than other models
long context retention: can work with your entire codebase without forgetting what you discussed 50 messages ago
security awareness: naturally includes input validation, sql injection prevention, proper error handling
code review quality: excellent at spotting potential issues in existing code
architecture suggestions: actually understands system design concepts and trade-offs
consistency: maintains architectural decisions throughout long conversations

limitations:

speed: noticeably slower than gpt for simple tasks
creativity: less likely to suggest novel approaches or creative solutions
overly cautious: sometimes refuses to generate code that's perfectly fine but could theoretically be misused
verbose explanations: can over-explain simple concepts

real example: building a payment processing system with stripe. claude not only generated the payment flow but proactively added webhook verification, idempotency keys, proper error logging, and even suggested implementing retry logic for failed payments. gpt would've given me the basic payment intent creation and called it done

getting the most out of claude:

leverage the long context window - paste your entire project structure
ask for security review of generated code
request architectural feedback before implementing major features
use it for code refactoring - it's excellent at improving existing code while maintaining functionality

claude shines when you need reliable, production-ready code. it's less likely to generate something that works in demo but fails in production.

google (gemini) - the speed demon with precision issues

gemini is fast. like, scary fast. but it requires a completely different approach and understanding of its strengths.

where gemini excels:

raw speed: generates code almost instantly
mathematical accuracy: complex algorithms, data structures, mathematical computations
optimization focus: naturally writes more efficient code
google cloud integration: seamless with gcp services
multimodal capabilities: can generate code from ui mockups or diagrams
technical precision: excellent at implementing specific algorithms or data structures

the gotchas:

prompt sensitivity: small changes in wording dramatically affect output quality
less conversational: doesn't handle back-and-forth refinement as well
documentation gaps: generates working code but minimal explanations
context limitations: struggles with large, complex projects
inconsistent quality: can produce brilliant code or completely miss the mark

real example: needed to implement a complex sorting algorithm for a data visualization. gemini delivered a perfect implementation in 30 seconds that was more efficient than what i would've written. but when i asked it to modify the algorithm slightly, it basically rewrote everything instead of making the small change

gemini optimization techniques:

structure prompts with clear sections (requirements, constraints, expected output)
include performance requirements upfront
provide concrete examples of input/output
ask for specific optimizations (time complexity, memory usage, etc)
be very precise about what you want changed when iterating

gemini works best when you know exactly what you want and can communicate it clearly. it's less forgiving of vague requirements but can deliver exceptional results when properly prompted.

the lovable factor: how the platform changes everything

what makes testing these models in lovable unique is the full-stack context. traditional ai coding assistants work in isolation - you're asking them to write a function or component without understanding how it fits into the bigger picture.

lovable gives these models something they've never had before: complete application context. when you ask claude to "add user authentication," it understands that means:

generating the login/register ui components
creating the backend api routes
setting up the database schema
configuring supabase auth
implementing proper error handling across all layers
ensuring the auth state management works with the existing app structure

this is why lovable's growth story is so compelling - they hit $17 million arr in 90 days because they solved the integration problem that makes ai coding tools frustrating for real projects.

the supabase integration advantage

one thing that becomes clear when using these models in lovable is how the supabase integration changes their behavior. instead of generating generic database code, they're working with a specific, well-documented backend-as-a-service platform.

claude particularly excels here - it understands supabase's row level security, realtime subscriptions, and edge functions. gpt is decent but sometimes generates supabase code that doesn't follow best practices. gemini is technically accurate but doesn't leverage supabase's unique features as effectively.

the stuff they all struggle with

state management

none of them really understand complex state flows. they'll generate redux actions that work in isolation but create race conditions in real apps. react context gets mangled when there are multiple providers. zustand stores work fine until you need complex selectors.

async operations

promise chains, concurrent api calls, proper error handling in async contexts - this is where bugs live. they all generate async code that works in the happy path but fails when networks are slow or apis return unexpected responses.

real-world data

they assume clean, well-structured data. real apis return inconsistent formats, missing fields, weird edge cases. none of the models handle this gracefully without explicit instruction.

performance at scale

generated code works fine with 10 records, falls apart with 10,000. pagination, virtualization, efficient queries - these require human oversight.

testing integration

unit tests are fine, integration tests are hit or miss, e2e tests are basically impossible without significant human intervention.

practical workflow that actually works

after months of trial and error, here's my current process that leverages each model's strengths:

phase 1: architecture planning (claude)

start with claude for overall system design. describe the feature, get architectural feedback, understand the trade-offs. claude's long context and reasoning abilities make it ideal for this phase.

phase 2: rapid prototyping (openai)

once architecture is solid, use gpt for fast iteration on ui components and basic functionality. its conversational nature makes it perfect for rapid back-and-forth during the creative phase.

phase 3: optimization and algorithms (gemini)

when you need specific performance optimizations or complex algorithms, switch to gemini. its speed and mathematical precision shine here.

phase 4: code review and security (claude)

bring everything back to claude for final review, security audit, and refactoring. its cautious nature and security awareness catch issues the other models miss.

the lovable multiplier effect

what's fascinating is how these models perform differently in lovable's full-stack environment versus traditional coding assistants:

better integration understanding: they see how frontend changes affect backend requirements
smarter defaults: knowing the supabase stack means better architectural decisions
fewer integration bugs: understanding the deployment target reduces compatibility issues
faster iteration: the visual editor combined with ai generation creates a feedback loop that accelerates development

real-world project breakdown: building a saas analytics dashboard

recently built a saas analytics dashboard using all three models in lovable - here's the detailed breakdown:

project scope:

user authentication and role-based access
real-time analytics dashboard
data export functionality
payment integration with stripe
responsive design across devices

model usage strategy:

claude (40% of interaction time):

overall system architecture and database schema
user authentication and role-based access implementation
stripe integration with proper webhook handling
security review and error handling improvements
code refactoring and optimization suggestions

gpt (50% of interaction time):

react dashboard components and ui elements
api endpoint generation and basic crud operations
responsive design implementation
user onboarding flow and ui/ux improvements
rapid iteration on feature requirements

gemini (10% of interaction time):

complex analytics calculations and data aggregation
chart performance optimization for large datasets
efficient database query optimization
mathematical functions for statistical analysis

results:

total development time: ~40 hours (would've been 80+ without ai)
ai-generated code percentage: ~60%
time spent debugging ai code: ~25% of total
production-ready features: 95% (only minor tweaks needed)

key insights:

the lovable platform's context awareness meant fewer integration issues. models understood how changes in one part of the app affected others. this reduced debugging time significantly compared to using ai assistants in traditional development environments.

credit-saving strategies (because they add up fast)

lovable's pricing is based on message limits, so efficiency matters:

draft prompts externally: write prompts in chatgpt/claude web first, refine them, then use in lovable
batch related requests: ask for multiple related functions in one prompt rather than separate messages
use code review mode: paste working code and ask for improvements rather than generating from scratch
leverage chat history: reference previous code instead of re-explaining context
be specific upfront: detailed requirements in the first prompt save multiple clarification rounds
use the visual editor: make simple ui changes visually instead of prompting for them

the uncomfortable truth about ai coding

it's not replacing developers, but it's definitely changing what we do. i spend way less time writing boilerplate and way more time on:

system design and architecture decisions
code review and quality assurance
user experience and product strategy
debugging complex integration issues
performance optimization and scaling considerations
understanding business requirements and translating them to technical specs

the junior developer who only knows how to implement features from detailed specs is in trouble. the senior developer who understands systems, trade-offs, user needs, and can effectively collaborate with ai is more valuable than ever.

looking ahead: the ai coding landscape in 2025

the models are improving fast. the gap between gpt-4 and the current generation is massive. anthropic's claude opus 4 and sonnet 4 represent a new generation of frontier models, while openai's rumored gpt-5 "arrakis" is expected to support rich multimodal interaction with parameter counts above 52 trillion.

but the fundamental limitations remain:

they don't understand your business context
they can't make product decisions
they struggle with complex system integration across multiple services
they need constant human oversight for production code
they don't understand user behavior and real-world usage patterns

the platform advantage

what lovable has done is solve the context problem. instead of asking models to generate code in a vacuum, they're working within a complete application framework. this changes everything:

models make better architectural decisions
integration bugs decrease significantly
deployment and hosting are handled automatically
team collaboration becomes seamless

this is why lovable's growth trajectory is so remarkable - from gpt engineer's open source viral moment to $17 million arr in 90 days. they've created an environment where ai coding actually works for real projects.

the weekend showdown: what to test

with unlimited free access during the ai showdown weekend, you can finally test all three models side-by-side in the same environment. here's what i'd recommend testing:

for each model, try building:

a simple crud app (todos, inventory, etc.) - tests basic competency
a dashboard with real-time updates - tests complex state management
user authentication with role-based access - tests security understanding
a form with complex validation - tests error handling
integration with external apis - tests real-world connectivity

pay attention to:

first attempt quality: how much works without iteration?
error handling: do they anticipate edge cases?
code organization: is the generated code maintainable?
security considerations: are best practices followed?
performance: does the code scale beyond toy examples?

bottom line

ai coding tools are legit game-changers if you know how to use them. they're terrible if you expect them to build your app for you without guidance or context.

treat them like really smart junior developers - excellent at implementation when given clear requirements, need guidance on architecture, require code review for anything important.

each model has its strengths:

openai: best for rapid prototyping and natural conversation
claude: best for production code and security-conscious development
gemini: best for performance-critical algorithms and technical precision

but the real magic happens when you use them in the right environment. lovable has created that environment - one where ai models have the context they need to generate production-ready applications instead of isolated code snippets.

the future isn't about replacing developers with ai. it's about creating environments where humans and ai can collaborate effectively to build better software faster.

what's your experience been? are you seeing similar patterns or completely different results?

also curious - which model do you reach for first when starting a new feature? and if you're jumping into the showdown this weekend, what are you planning to build?

this analysis is based on 8 months of real-world usage in lovable, building everything from simple prototypes to production saas applications. your mileage may vary, but these patterns have been consistent across dozens of projects.

The "Grant Guardian": Automated Grant Proposal Assistant

Taki Tajwaruzzaman Khan — Sat, 07 Jun 2025 05:51:17 +0000

This is a submission for the Runner H "AI Agent Prompting" Challenge

What I Built

The "Grant Guardian": Automated Grant Proposal Assistant

I built an autonomous AI agent, powered by Runner H, designed to significantly streamline the grant proposal writing process for non-profits, researchers, and small businesses. This agent leverages Google Sheets to manage project details, budget breakdowns, and funder requirements, and Google Docs to generate tailored proposal narratives, executive summaries, and supporting documents. The problem it solves is the immense time, effort, and specialized knowledge required to craft compelling and compliant grant applications, often leading to missed funding opportunities or burnout for organizations with limited resources. The Grant Guardian ensures proposals are well-structured, persuasive, and aligned with funder guidelines, dramatically increasing the chances of securing vital funding.

Demo

How I Used Runner H

My Runner H agent orchestrates a meticulous and efficient grant proposal workflow, seamlessly integrating Google Sheets for structured data management and Google Docs for dynamic content generation, all guided by intelligent prompting techniques.

Here\"s a breakdown of the workflow and how Runner H is utilized:

1. Project & Funder Requirements Management (Google Sheets & Few-shot Prompting)

Runner H begins by establishing the core project details and funder requirements within interconnected Google Sheets:

Project_Details.gsheet: Stores project overview (Title, Objectives, Methodology, Expected Outcomes, Timeline).
Budget_Breakdown.gsheet: Manages detailed budget categories (Personnel, Equipment, Travel, Supplies, Indirect Costs) and justifications.
Funder_Requirements.gsheet: Catalogs specific guidelines from grant announcements (Eligibility, Narrative Sections, Word Limits, Formatting, Submission Deadline).
Supporting_Documents.gsheet: Lists required attachments (e.g., CVs, Letters of Support, Financial Statements) and their status.

Runner H assists in populating these sheets. For instance, when the user inputs a funder name or grant program, Runner H, using Few-shot Prompting (drawing from examples of common grant requirements or typical project structures), can pre-populate the Funder_Requirements.gsheet with standard sections and suggest budget categories for the Budget_Breakdown.gsheet. This ensures all necessary information is captured and organized from the outset.

2. Dynamic Proposal Narrative Generation (Google Docs & Meta Prompting)

Based on the structured data in the Google Sheets, Runner H dynamically generates various sections of the grant proposal in Google Docs:

Executive Summary: Crafts a concise and compelling overview.
Project Narrative: Develops detailed sections on objectives, methodology, and expected outcomes.
Budget Justification: Writes persuasive explanations for each budget item.
Impact Statement: Articulates the broader significance and potential benefits of the project.

In this phase, Meta Prompting is crucial. Runner H is prompted to generate the optimal tone, persuasive language, and structure for each proposal section, adhering to the guidelines in Funder_Requirements.gsheet. For example, the user might prompt: "Generate the 'Project Narrative' section for the 'Community Garden Initiative' proposal, emphasizing sustainability and community engagement, with a word limit of 1500 words." Runner H then intelligently synthesizes information from Project_Details.gsheet and Budget_Breakdown.gsheet, ensuring compliance and persuasive storytelling, and writes it directly into a new Google Doc (e.g., "Grant_Proposal_Community_Garden.docx").

3. Compliance Checking & Gap Identification (Google Sheets & Active-Prompt)

Runner H continuously cross-references the generated content in Google Docs with the Funder_Requirements.gsheet. It acts as a vigilant compliance officer, identifying missing sections, exceeding word limits, or non-adherence to formatting rules. If a compliance issue or a data gap is detected, Runner H uses Active-Prompt to alert the user and suggest immediate corrective actions. For example: "The 'Methodology' section is 200 words over the limit. Would you like me to summarize key points, or should I highlight areas for manual reduction?" or "Missing 'Letters of Support' in Supporting_Documents.gsheet. Please upload or update status." This proactive approach ensures proposals are fully compliant before submission.

4. Iterative Refinement & Persuasion Enhancement (Google Docs & Chain-of-Thought Prompting)

Runner H assists in refining the proposal for maximum impact. Users can request revisions or ask for improvements in specific areas. Runner H employs Chain-of-Thought (CoT) Prompting to reason through persuasive writing principles. For example, if the user asks: "How can I make the 'Impact Statement' more compelling?" Runner H might reason: "To enhance impact, consider quantifiable metrics, clear beneficiary descriptions, and a strong call to action. What specific outcomes can be highlighted?" and then suggest specific phrasing or data points to add to the Google Doc. This iterative reasoning helps in crafting a highly persuasive narrative.

5. Adaptive Learning & Success Optimization (Reflexion)

To continuously improve its grant proposal writing capabilities, Runner H incorporates Reflexion. After each grant submission (and ideally, after receiving feedback on successful or unsuccessful applications), it implicitly evaluates the effectiveness of its generated content and compliance checks. If a proposal was rejected due to a specific section, Runner H self-corrects its internal logic and adjusts its prompting strategies for future proposals. For example, if proposals consistently lack strong budget justifications, Runner H will refine its Meta Prompting for that section or enhance its Few-shot suggestions for budget categories. This iterative self-improvement ensures that the agent becomes an increasingly effective grant-winning assistant.

Use Case & Impact

Who Benefits:

This Runner H workflow is invaluable for:

Non-profit Organizations: Dramatically increases their capacity to apply for and secure funding, enabling them to expand their programs and impact.
Academic Researchers: Frees up valuable research time by automating the tedious process of grant application writing, allowing them to focus on scientific discovery.
Small Businesses & Startups: Provides a professional and efficient way to apply for grants, venture capital, or other funding opportunities without hiring dedicated grant writers.
Grant Writers & Consultants: Augments their capabilities, allowing them to manage more clients and deliver higher-quality proposals in less time.
Community Development Initiatives: Empowers grassroots organizations to access funding for local projects.

Impact:

Increased Funding Success Rates: By ensuring compliance, persuasiveness, and thoroughness, the agent significantly improves the likelihood of securing grants.
Significant Time & Resource Savings: Automates repetitive and time-consuming tasks, freeing up staff to focus on core mission activities.
Reduced Stress & Burnout: Alleviates the pressure associated with complex grant applications, making the process more manageable.
Enhanced Proposal Quality: Generates well-structured, articulate, and compelling narratives that stand out to funders.
Improved Compliance & Accuracy: Minimizes errors and ensures all funder requirements are met, reducing rejection rates due to technicalities.
Democratization of Funding Access: Lowers the barrier to entry for organizations with limited grant writing experience or resources.

Unique Angle:

While there are tools for project management or document creation, the "Grant Guardian" uniquely leverages Runner H to intelligently orchestrate the entire grant proposal lifecycle within the familiar and collaborative environment of Google Sheets and Google Docs. It doesn't just store data or generate text; it reasons about funder requirements, proactively identifies compliance gaps, enhances persuasive language, and learns from past outcomes. This transforms Google Workspace into a dynamic, intelligent grant-writing powerhouse, offering a level of automation, compliance assurance, and strategic assistance typically found only in expensive, specialized grant management software, but with the accessibility and flexibility of Google's ecosystem.

Full Prompt:

Runner H, execute the following workflow as an autonomous AI agent, acting as a "Grant Guardian: Automated Grant Proposal Assistant." Your goal is to streamline the grant proposal writing process for non-profits, researchers, and small businesses, leveraging Google Sheets to manage project details, budget breakdowns, and funder requirements, and Google Docs to generate tailored proposal narratives, executive summaries, and supporting documents. Prioritize compliance, persuasiveness, efficiency, and increased funding success rates. If any step encounters an issue or requires clarification, use Active-Prompt to seek guidance.

### Workflow Steps:

**1. Project & Funder Requirements Management (Google Sheets):**
   - **Action:** Establish core project details and funder requirements within interconnected Google Sheets. If a sheet doesn\"t exist, create it.
     - **Project_Details.gsheet:** Columns: Project Title, Objectives, Methodology Summary, Expected Outcomes, Timeline (Start Date, End Date).
     - **Budget_Breakdown.gsheet:** Columns: Category (e.g., Personnel, Equipment), Item, Quantity, Unit Cost, Total Cost, Justification.
     - **Funder_Requirements.gsheet:** Columns: Section Name (e.g., Executive Summary, Project Narrative), Word Limit, Formatting Guidelines, Required Attachments.
     - **Supporting_Documents.gsheet:** Columns: Document Name, Type (e.g., CV, Letter of Support), Status (e.g., To Do, In Progress, Completed), File Path/Link.
   - **Technique:** Use Few-shot Prompting to pre-populate sheets with common grant sections, budget categories, or document types based on the type of grant or organization. Ensure data is structured and consistent.
   - **Output:** Organized and populated Google Sheets serving as the grant project\"s central database.

**2. Dynamic Proposal Narrative Generation (Google Docs):**
   - **Action:** Dynamically generate various sections of the grant proposal in Google Docs based on data from the Sheets.
   - **Technique:** Utilize Meta Prompting to generate the optimal tone, persuasive language, and structure for each proposal section, adhering to guidelines in `Funder_Requirements.gsheet`. Write directly into a new Google Doc (e.g., "[Project Title]_Grant_Proposal.docx").
   - **Output:** Professionally drafted and tailored grant proposal sections in Google Docs.

**3. Compliance Checking & Gap Identification (Google Sheets & Google Docs):**
   - **Action:** Continuously cross-reference generated content in Google Docs with `Funder_Requirements.gsheet` for compliance. Identify missing information or deviations.
   - **Technique:** Use Active-Prompt to alert the user and suggest immediate corrective actions if a compliance issue (e.g., exceeding word limit, missing section) or data gap is detected. For example: "The \"[Section Name]\" is [X] words over the limit. Would you like me to summarize, or highlight areas for manual reduction?" or "Missing \"[Document Name]\" in Supporting_Documents.gsheet. Please upload or update status."
   - **Output:** Proactive alerts and guidance for ensuring full proposal compliance.

**4. Iterative Refinement & Persuasion Enhancement (Google Docs):**
   - **Action:** Assist in refining the proposal for maximum impact and persuasiveness.
   - **Technique:** Employ Chain-of-Thought (CoT) Prompting to reason through persuasive writing principles. For example, if asked to make a section more compelling, reason about quantifiable metrics, clear beneficiary descriptions, and strong calls to action. Suggest specific phrasing or data points to add to the Google Doc.
   - **Output:** A highly refined and persuasive grant proposal narrative.

**5. Adaptive Learning & Success Optimization (Google Sheets):**
   - **Action:** Continuously improve grant proposal writing capabilities.
   - **Technique:** Incorporate Reflexion. After each grant submission (and ideally, after receiving feedback), implicitly evaluate the effectiveness of generated content and compliance checks. If a proposal was rejected due to a specific section, self-correct internal logic and adjust prompting strategies for future proposals (e.g., refine Meta Prompting for persuasive language, or enhance Few-shot suggestions for budget justifications).
   - **Output:** An increasingly effective grant-winning assistant.

Behind the Scenes of AI: How Language Models Like ChatGPT Work

Taki Tajwaruzzaman Khan — Tue, 25 Jun 2024 01:48:46 +0000

If you’ve ever wondered how an AI like ChatGPT can understand and generate text that feels almost human, today you’re in for a treat! Today, I want to show you what goes on under the hood, explained in a way that’s easy to grasp.

Collecting and Prepping Data

At it’s core, ChatGPT is just a finest engineering of pre-existing data. Think of ChatGPT as a sponge that needs to soak up information before it can start ‘thinking’ (bad analogy, sorry :3). This data comes from a wide array of sources: books, articles, websites, and more. The diversity of the data is crucial because it helps the AI understand various contexts, languages, dialects, and writing styles.

After collecting data, it goes through preprocessing. Here, words are broken into smaller units called tokens. This process, which includes techniques like Byte Pair Encoding, helps the AI manage new or unusual words it might encounter later.

Building the Brain: The Neural Network

The core of ChatGPT is built on what’s known as the Transformer architecture — a complex neural network design that helps the AI focus on different parts of a sentence to understand context better. Each layer of this network uses self-attention mechanisms that analyze the importance of each word in relation to others, akin to keeping track of multiple storylines in a novel.

Making Sense of Order: Encoding

In the digital world of ChatGPT, words are initially treated as a list with no inherent order. Positional encoding is used to add information about the position of each word in the sequence, allowing the AI to understand which word comes first, second, and so on.

Learning Through Trial and Error: Training

Training ChatGPT involves feeding it large amounts of text and using its predictions to teach it correct responses. The AI learns through a method called backpropagation, where errors are used to make adjustments to improve accuracy. This is done using algorithms like Adam or stochastic gradient descent, which fine-tune the model’s parameters to minimize prediction errors.

How Does ChatGPT Talk Back? The Generation Process

Generating text involves several strategies:

Greedy Sampling: Choosing the most probable next word each time.
Beam Search: Considering multiple possible future sequences to find the most likely one.
Top-k Sampling: Limiting predictions to a set number of top choices, which reduces the chance of bizarre responses.
Top-p Sampling: Choosing from a dynamic number of top probabilities, balancing creativity and coherence.

Fine-Tuning: Getting Specific

For tasks requiring specialized knowledge, like legal or medical advice, ChatGPT can be fine-tuned on domain-specific datasets. This process is akin to a doctor attending specialized medical training after general medical school.

Keeping It Real: Evaluation

ChatGPT’s performance is evaluated using metrics like perplexity, which measures how well the model predicts a sample, and BLEU, which assesses the quality of text translation against reference texts. However, the true measure often involves human evaluators who assess the model’s outputs for relevance, coherence, and naturalness.

Keeping It Fair: Bias and Fairness

Ensuring that ChatGPT remains unbiased is a critical challenge. Developers continuously analyze and adjust the training data and tweak algorithms to mitigate biases, aiming for a fair and balanced AI.

Wrap-Up

With these insights, you can appreciate the intricate blend of massive data processing, advanced neural networks, continuous learning, and careful human oversight that powers ChatGPT. Each interaction with this AI isn’t just a display of technical prowess but also a testament to the ongoing efforts to make technology more responsive and responsible. So, the next time you engage with ChatGPT, remember the incredible technology and diligent human work crafting those responses!

Wanna Start HTML CSS to Embark on Your Web Development Journey?

Taki Tajwaruzzaman Khan — Sun, 12 Nov 2023 23:10:30 +0000

Starting HTML and CSS as a beginner can seem intimidating, but with a little guidance and some practice, it can be a fun and rewarding experience. Here are some steps to get you started:

1. Learn the basics: Before you dive into coding, it's important to understand the fundamentals of HTML and CSS. This includes understanding what they are, what they do, and how they work together to create websites. There are plenty of online resources, such as tutorials and guides, that can help you get a grasp on these concepts.

2. Set up your development environment: To start coding, you'll need a text editor to write your code in and a web browser to view your progress. There are many free options available, such as Notepad++ or Sublime Text for text editors, and Google Chrome or Mozilla Firefox for web browsers.

3. Create your first HTML file: HTML stands for HyperText Markup Language, and it's used to structure the content on a webpage. To create your first HTML file, simply open up your text editor and type in the following:

<!DOCTYPE html>
<html>
  <head>
    <title>My First HTML Page</title>
  </head>
  <body>
    <h1>Welcome to my website!</h1>
    <p>This is my first time coding in HTML and CSS, and I'm excited to learn more!</p>
  </body>
</html>

4. Save your HTML file: Once you've typed in the above code, save your file as "index.html". This will be the main file that your web browser loads when you visit your website.

5. View your HTML file in a web browser: To view your HTML file in a web browser, simply double-click on the file or drag it into your web browser window. You should see your heading and paragraph displayed on the page.

6. Add CSS: Now that you've got the basics of HTML down, you can start adding some style to your webpage with CSS (Cascading Style Sheets). To do this, create a new file called "style.css" and link it to your HTML file by adding the following line of code to the head of your HTML file:

<link rel="stylesheet" type="text/css" href="style.css">

7. Start styling: In your CSS file, you can add rules that specify how certain elements on your webpage should look. For example, to make all of your headings red, you could add the following rule:

h1 {
    color: red;
}

8. Practice, practice, practice: As with any skill, the more you practice coding in HTML and CSS, the better you'll become. Don't be afraid to experiment with different techniques and styles, and don't be discouraged if you encounter errors or challenges along the way.

With a little perseverance and patience, you'll be a pro in no time!

DEV Community: Taki Tajwaruzzaman Khan

building a medical ai platform that doctors actually want to use

what i actually built

demo: how it actually works in practice

the emergency department scenario

the complex case workflow

what doctors actually said during testing

the mcp architecture that actually works

the multi-server orchestration approach

why this approach crushes traditional medical search

the technical challenges nobody talks about

what makes this different from basic mcp usage

what i learned building this thing

the uncomfortable truth about medical ai

the mcp breakthrough moment

what actually works in production

the real-world impact metrics

scaling challenges nobody prepares you for

what's next for medical mcp applications

bottom line

can ai actually code? real talk after 8 months on lovable

the reality check nobody talks about

openai models - the swiss army knife that sometimes cuts you

what they're actually good at:

where they fall apart:

prompting strategies that actually work:

anthropic (claude) - the careful craftsman

what makes claude special:

limitations:

getting the most out of claude:

google (gemini) - the speed demon with precision issues

where gemini excels:

the gotchas:

gemini optimization techniques:

the lovable factor: how the platform changes everything

the supabase integration advantage

the stuff they all struggle with

state management

async operations

real-world data

performance at scale

testing integration

practical workflow that actually works

phase 1: architecture planning (claude)

phase 2: rapid prototyping (openai)

phase 3: optimization and algorithms (gemini)

phase 4: code review and security (claude)

the lovable multiplier effect

real-world project breakdown: building a saas analytics dashboard

project scope:

model usage strategy:

results:

key insights:

credit-saving strategies (because they add up fast)

the uncomfortable truth about ai coding

looking ahead: the ai coding landscape in 2025

the platform advantage

the weekend showdown: what to test

for each model, try building:

pay attention to:

bottom line

The "Grant Guardian": Automated Grant Proposal Assistant

What I Built

Demo

How I Used Runner H

1. Project & Funder Requirements Management (Google Sheets & Few-shot Prompting)

2. Dynamic Proposal Narrative Generation (Google Docs & Meta Prompting)

3. Compliance Checking & Gap Identification (Google Sheets & Active-Prompt)

4. Iterative Refinement & Persuasion Enhancement (Google Docs & Chain-of-Thought Prompting)

5. Adaptive Learning & Success Optimization (Reflexion)

Use Case & Impact

Full Prompt:

Behind the Scenes of AI: How Language Models Like ChatGPT Work

Wanna Start HTML CSS to Embark on Your Web Development Journey?

Thank you & Best Wishes!