Forem: Keith Fawcett

Getting Started with Coherence API: Your First Integration

Keith Fawcett — Tue, 21 Apr 2026 17:00:40 +0000

Getting Started with Coherence API: Your First Integration

Prerequisites

Coherence account (free tier works)
API key from dashboard
Your favorite HTTP client

Authentication

curl -H "Authorization: Bearer YOUR_API_KEY" \
  https://api.getcoherence.io/v1/workspaces

Create Your First Lead

import { Coherence } from '@coherence/sdk';

const coherence = new Coherence({
  apiKey: process.env.COHERENCE_API_KEY
});

const lead = await coherence.leads.create({
  name: 'Jane Smith',
  email: 'jane@lawfirm.com',
  company: 'Big Law LLP',
  source: 'linkedin',
  score: 85,
  tags: ['legal-tech', 'enterprise']
});

console.log(`Created lead: ${lead.id}`);

Set Up Webhooks

await coherence.webhooks.create({
  url: 'https://your-app.com/webhooks/coherence',
  events: [
    'lead.created',
    'deal.stage_changed',
    'deal.at_risk'
  ]
});

Next Steps

Explore the full API reference
Check out our SDK examples
Join the developer community

docs.getcoherence.io →

Ask HN: What's your actual CRM workflow as a solo founder?

Keith Fawcett — Sun, 19 Apr 2026 16:00:03 +0000

Ask HN: What's your actual CRM workflow as a solo founder?

Building Coherence (autonomous AI CRM for FinTech), trying to understand real workflows.

Specifically interested in:

How much time do you actually spend on CRM tasks per week?
What percentage of that is:
- Manual data entry
- Follow-up drafting
- Compliance documentation
- Report generation
- Other
What's the most annoying CRM task you do repeatedly?
If you could automate one CRM task forever, what would it be?

We're seeing lots of "AI-powered" CRM tools, but most just add chatbots or suggestions.

Not actual autonomous execution.

Curious if the HN crowd has different workflows than our typical FinTech founder user.

Genuinely trying to build something useful here, not just mining for ideas.

What's your actual workflow?

AskHN #CRM #FinTech #Startups

Webhook Reliability: Why Your CRM's Webhooks Are Probably Lying to You

Keith Fawcett — Fri, 17 Apr 2026 10:00:04 +0000

Webhook Reliability: Why Your CRM's Webhooks Are Probably Lying to You

Your CRM says it has webhooks. It probably does.

But if you're building critical workflows on them, you're likely building on sand.

Let me explain why.

The Webhook Reality

Most CRM webhooks are "best effort":

No delivery guarantees
No retry policies with backoff
No dead letter queues
No visibility into what's happening

You send a test webhook, it works. You deploy to production, things break silently.

What "Reliable Webhooks" Actually Means

// Reliable webhook delivery
const result = await coherence.webhooks.subscribe({
  event: 'deal.*',
  url: 'https://your-app.com/webhook',
  retryPolicy: {
    maxRetries: 5,
    backoff: 'exponential',
    timeout: 30000
  },
  filtering: {
    stage: ['proposal', 'negotiation']
  }
});

// Webhook status tracking
const status = await coherence.webhooks.getDeliveryStatus(result.id);
// { delivered: true, attempts: 3, lastAttempt: '2026-04-15T10:00:00Z' }

The Key Differences

Feature	Bad Webhooks	Reliable Webhooks
Delivery	Best effort	Guaranteed with retry
Visibility	None	Full delivery tracking
Filtering	None or basic	Advanced event filtering
Error Handling	Silent failures	Dead letter queues
Testing	Manual only	Test mode with replay

The FinTech Implication

For FinTech applications, unreliable webhooks aren't just annoying—they're compliance risks.

When payment events don't propagate:

Compliance logs are incomplete
Audit trails have gaps
You might miss critical events

Testing Your Webhooks

Before you trust any CRM's webhooks:

Test mode that simulates failures
Replay capability for missed events
Delivery status for every webhook
Alerting when something fails

If your CRM doesn't have these, you're one network blip away from a problem.

What's your webhook horror story? Let's hear it in the comments.

Webhooks #API #FinTech #Development #CRM

I Built a CRM That Actually Works for How Developers Think

Keith Fawcett — Tue, 14 Apr 2026 14:00:24 +0000

I Built a CRM That Actually Works for How Developers Think

After years of watching perfectly good CRMs gather dust because "nobody wants to update it," I finally understood why.

The problem isn't the tools. It's that CRMs were built for sales reps, not engineers.

The Developer CRM Problem

Every developer I've talked to shares the same complaints:

"Our engineers spend 3+ hours/week on manual CRM updates"
"Our pipeline data is always stale by the time I see it"
"We built custom integrations but they break constantly"

Sound familiar?

Why CRMs Fail Developers

1. Manual Data Entry is Anti-DRY

As developers, we live by DRY principles. Don't Repeat Yourself.

But CRMs? They're built on repeat-after-me.

Email a new prospect? Retype everything into the CRM.
Close a deal? Update 5 fields manually.
Get meeting notes? Copy-paste into the system.

This isn't just annoying. It violates everything we believe about software design.

2. Stale Data is Unacceptable

In engineering, we have tests, CI/CD, observability. We know when things break.

But in most CRMs? The data is stale the moment you enter it.

By the time a manager sees the pipeline, it's already outdated.

3. Integrations Break Constantly

Custom integrations are brittle. When something changes (API, webhook, auth), everything breaks.

And who's stuck fixing it? The developer.

So I Built Coherence

Not because the market needed another CRM. Because developers needed a CRM that works the way they think.

The core principle: Instead of asking humans to update the CRM, the CRM updates itself.

How It Works

1. AI agents monitor: Email, Calendar, Slack, GitHub
2. Auto-create/update: Contacts, Deals, Tasks, Notes
3. Surface insights: Without any manual data entry
4. Trigger actions: Based on signals, not schedules

The API-First Design

Everything in Coherence is API-accessible:

// Create a contact
const contact = await coherence.contacts.create({
  email: 'dev@example.com',
  name: 'Sarah Chen',
  company: 'TechCorp'
});

// AI agent automatically enriches with:
// - Company data from web research
// - Social profiles
// - Recent activity
// - Engagement history

Integration Architecture

// Webhooks for real-time sync
coherence.webhooks.on('deal.updated', async (event) => {
  await syncToExternalSystem(event.data);
});

// Zapier/Make integration
// Native integrations with 100+ tools
// REST API for custom integrations

Real Results

From early customers:

Metric	Before	After
CRM data entry time	3+ hrs/week	45 min/week
Pipeline report accuracy	60%	95%
Lead response time	4 hours	15 minutes
Forecast accuracy	40%	78%

What This Means for Developer Teams

No more manual entry: AI handles data capture
Real-time accuracy: Pipeline updates automatically
API-first: Build whatever you need on top
Your stack, your rules: Works with GitHub, Linear, Notion, etc.

The Technical Stack

For the curious:

Backend: Node.js + PostgreSQL
AI: GPT-4 + Claude for agent logic
Real-time: WebSockets for live updates
API: REST + GraphQL
Integrations: Zapier, Make, native webhooks

What I'd Do Differently

If I were building this again:

Start with the API — The UI should wrap the API, not the other way around
Make agents configurable — Users should control what the AI does
Design for failure — Integrations break; handle it gracefully

Try It

I'm not going to pretend this isn't a pitch. It is.

But if you're a developer who's frustrated with CRMs, I think you'll find Coherence different.

What CRM pain point would you pay anything to solve?

👇 Comments welcome.

How AI Agents Are Replacing Traditional SaaS: A Developer's Guide to Autonomous CRM

Keith Fawcett — Sun, 12 Apr 2026 02:02:51 +0000

How AI Agents Are Replacing Traditional SaaS: A Developer's Guide to Autonomous CRM

The Evolution: From Tools to Agents

For decades, software has been a tool we operate. We click buttons, fill forms, and trigger actions. The software responds to our commands but never initiates.

AI agents represent a paradigm shift: software that observes, decides, and executes autonomously. Instead of "I use software to do X," the new model is "my AI agent handles X while I focus on strategy."

This shift is particularly transformative for business infrastructure like CRMs, where the operational overhead of traditional tools often exceeds their value.

Understanding Autonomous Agents in CRM Context

What Makes an Agent "Autonomous"?

Traditional Software:
User → Action → Software Response → User Reviews → User Approves → Action Executes

Autonomous Agent:
Context → Observation → Decision → Execution → Learning → Context Update
         (continuous, no prompts required)

Key Capabilities of Autonomous CRM Agents

1. Contextual Awareness
Agents maintain persistent context across the entire CRM—contacts, deals, emails, documents, and historical interactions. They understand relationships between entities without requiring manual linking.

2. Scheduled Execution
Agents run on schedules (hourly, nightly, weekly) without prompts. They execute recurring tasks like lead research, follow-up drafting, and CRM updates automatically.

3. Pattern Recognition
Agents observe your behavior and learn patterns. When you typically follow up, which types of leads convert, what information matters most—they adapt their execution accordingly.

4. Compound Learning
Each cycle improves the agent's performance. Feedback loops (approved, rejected, edited) train the agent on your preferences without explicit retraining.

Technical Architecture: How Agents Work Inside Coherence

The Agent Execution Model

┌─────────────────────────────────────────────────────────────┐
│                      Agent Architecture                       │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────────┐ │
│  │   Context   │───▶│  Decision   │───▶│    Execution    │ │
│  │  (Memory)   │◀───│   Engine    │◀───│    (Actions)    │ │
│  └─────────────┘    └─────────────┘    └─────────────────┘ │
│         ▲                  │                   │            │
│         │                  ▼                   ▼            │
│         │          ┌─────────────┐    ┌─────────────────┐  │
│         └──────────│   Learning  │◀───│   Feedback      │  │
│                    │    Loop     │    │   (Human)       │  │
│                    └─────────────┘    └─────────────────┘  │
└─────────────────────────────────────────────────────────────┘

Agent Types in Practice

Lead Research Agent

Observes: New leads in CRM
Decides: Which sources to research based on lead profile
Executes: Pulls data from LinkedIn, company websites, news
Learns: Which data points matter most for your ICP

Follow-up Draft Agent

Observes: Deal stage changes, meeting schedules, email threads
Decides: What type of follow-up is appropriate
Executes: Drafts personalized emails based on context
Learns: Your tone, formatting preferences, CTAs that work

CRM Hygiene Agent

Observes: Stale records, missing data, outdated information
Decides: What needs updating and how
Executes: Auto-fills fields, enriches contacts, archives dead deals
Learns: Your data quality standards

Comparison: Traditional CRM vs Agent-Powered CRM

Aspect	Traditional CRM	Agent-Powered CRM
Data Entry	Manual by users	Automatic from context
Lead Research	Manual or paid enrichment	Autonomous agent research
Follow-ups	User-initiated	Agent-scheduled drafts
CRM Updates	Click-based	Observation-based auto-update
Learning	Static (unless retrained)	Continuous from feedback
Onboarding	User training required	Agent learns from usage
Maintenance	Ongoing admin	Agent self-maintains

Real-World Agent Execution Example

Scenario: New Lead Enters CRM

Traditional CRM Flow:

Lead comes in via form/API
Sales rep manually reviews lead source
Sales rep researches on LinkedIn (10-15 min)
Sales rep manually enters enrichment data
Sales rep drafts personalized outreach (15-20 min)
Total: 25-35 minutes per lead

Agent-Powered CRM Flow:

Lead enters CRM automatically
Agent observes, immediately starts research
Agent drafts research summary and outreach
Agent schedules follow-up sequence
Sales rep reviews drafts (2-5 min)
Approves or edits with feedback
Total: 5 minutes per lead (agent handles research/drafting)

Difference: 30 minutes saved per lead × 50 leads/week = 25 hours/week recovered

Building with Autonomous CRM Agents

API Considerations for Agent Integration

When evaluating agent-powered CRMs, consider:

1. Event Webhooks
Agents need to observe CRM events in real-time. Look for comprehensive webhook support:

// Example: Subscribe to lead creation events
POST /api/webhooks
{
  "event": "lead.created",
  "url": "https://your-agent.endpoint/events",
  "actions": ["enrich", "draft_outreach", "update_context"]
}

2. Context Access
Agents need read access to all business context—not just CRM records:

Email history (sent/received)
Calendar events
Document repository
Communication threads

3. Action Capabilities
Agents should be able to execute, not just read:

Create/update records
Draft and queue communications
Schedule tasks and events
Trigger downstream automations

Integration Patterns

┌─────────────────────────────────────────────────────────────┐
│                   Agent Integration Patterns                 │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  Pattern 1: Event-Driven                                      │
│  ┌──────┐    ┌─────────┐    ┌───────┐    ┌──────────┐      │
│  │ CRM  │───▶│ Webhook │───▶│ Agent │───▶│ Action   │      │
│  └──────┘    └─────────┘    └───────┘    └──────────┘      │
│                                                              │
│  Pattern 2: Scheduled Polling                                │
│  ┌──────┐    ┌─────────┐    ┌───────┐    ┌──────────┐      │
│  │ Agent │──▶│ Poll    │───▶│ CRM   │───▶│ Execute  │      │
│  └──────┘    └─────────┘    └───────┘    └──────────┘      │
│                                                              │
│  Pattern 3: Continuous Monitoring                            │
│  ┌──────┐    ┌─────────────┐    ┌───────┐                   │
│  │ Agent │◀──│ Full Context │◀───│ CRM   │                   │
│  └──────┘    └─────────────┘    └───────┘                   │
│                                                              │
└─────────────────────────────────────────────────────────────┘

Evaluating Agent-Powered CRM Platforms

Technical Checklist

[ ] Agent can access full business context (emails, docs, calendar)
[ ] Scheduled execution without manual triggers
[ ] Learning from feedback loops
[ ] Compound improvement over time
[ ] Human-in-the-loop for approval workflows
[ ] Audit trail for agent decisions
[ ] Easy override/correction mechanisms
[ ] API access for custom integrations

Business Value Checklist

[ ] Measurable time savings per workflow
[ ] Improved lead response time
[ ] Consistent execution (no dropped balls)
[ ] Scalable without proportional headcount
[ ] ROI measurable within 30 days

The Future: Agent-Native Business Infrastructure

We're moving from "users of software" to "managers of agents." The companies winning in 2026 aren't just adopting AI—they're building agent-native operations where autonomous software handles execution while humans focus on strategy.

This isn't about replacing humans with AI. It's about eliminating the operational overhead that prevents founders from doing what only humans can do: making decisions, building relationships, and creating value.

The new question isn't "what can AI do for us?" It's "what should humans do while AI handles everything else?"

Explore autonomous CRM: Coherence
Developer API: getcoherence.io/developers

Why Developer-Founded Startups Need a CRM Built for Builders

Keith Fawcett — Fri, 10 Apr 2026 20:11:49 +0000

Why Developer-Founded Startups Need a CRM Built for Builders

When you build a startup as a developer, you think in systems, APIs, and workflows. You automate everything. Your CI/CD pipeline is airtight, your error monitoring is real-time, and your deploys are automated.

But then there's your CRM.

Most CRMs were built for sales teams, not builders. They're clunky, require manual data entry, and treat integrations as an afterthought. For developer-founded startups running lean, this is a massive friction point.

The Problem with Legacy CRMs for Technical Teams

If you're a developer building your startup, you've likely encountered this:

Your CRM doesn't have a proper API, so you're manually syncing data
Webhooks are unreliable or non-existent
Automation workflows are rigid and can't handle edge cases
Third-party integrations break constantly and require workarounds
You're spending engineering cycles just to make your CRM work

This is backwards. Your CRM should automate away work, not create more of it.

What Developer-Founded Startups Actually Need

The best CRMs for technical teams understand that:

APIs are first-class citizens — Every feature should be accessible via API. If you can't automate it, it doesn't scale.
Webhooks enable real-time sync — Your CRM should push data to your systems, not require your systems to poll for updates.
Automation should be programmable — When a lead hits certain criteria, triggers should fire across your entire stack—email sequences, Slack notifications, ticket creation, data enrichment.
Integrations should just work — Your CRM should connect to your existing tools (GitHub, Linear, Stripe, Intercom, Slack) without complex configuration or brittle workarounds.
Developer experience matters — Clean documentation, SDKs in multiple languages, and sandbox environments aren't nice-to-haves—they're requirements.

How Coherence Fits Into the Developer Stack

Coherence was built with developer workflows in mind. Here's what that looks like in practice:

API-First Architecture

Every action in Coherence is available via API. Create contacts, update deals, trigger automations, fetch reports—all programmatically. The API follows REST conventions and returns consistent JSON responses.

Powerful Webhooks

Configure webhooks to push data to your systems in real-time. When a deal closes, your accounting system gets notified. When a contact fills out a form, your Slack channel lights up. No polling, no delays, no manual triggers.

Automation That Thinks Like a Developer

Coherence's automation engine treats triggers and actions like a state machine:

IF contact.status == "qualified" 
AND deal.value > 5000
AND contact.source == "api_signup"
THEN:
  - Add to "High-Value API" segment
  - Trigger onboarding email sequence
  - Create Linear task for CS follow-up
  - Post to #deals Slack channel

This isn't a visual workflow builder with cryptic syntax—it's a rules engine you can configure programmatically.

Native Integrations That Developers Actually Use

GitHub: Link deals to repos, see commit activity from prospects
Stripe: Sync customer data, track MRR automatically
Intercom: Unify support conversations with CRM data
Slack: Real-time notifications and deal alerts
Linear: Create tasks directly from deal context

The Developer-Founded Startup Advantage

When your CRM is built for developers, you stop spending time on CRM administration and start using it strategically. You can:

Build custom reporting by querying the API and piping data to your BI tool
Create internal tools that surface CRM data in your product
Automate outreach sequences triggered by product events
Sync customer success data bidirectionally with your support stack

Your CRM becomes an extension of your engineering stack, not a separate tool your sales team uses in isolation.

The Bottom Line

If you're a developer-founded startup, your CRM should feel like the tools you love to use: programmable, well-documented, and built to scale with you. Coherence is designed for technical teams who want to automate their growth, not fight with their tools.

The best CRM for developers is the one you don't have to think about—one that just works, integrates seamlessly, and gets out of your way so you can focus on building.

Ready to see how Coherence works for your stack? Check out the API documentation and start automating your growth workflow today.

Developer CRM Manifesto - Ship24 Developer Community

Keith Fawcett — Thu, 09 Apr 2026 07:11:51 +0000

A CRM Built for Developers Looks Like This

For the developer community building shipping, e-commerce, and SaaS tools

The Problem

Most CRMs were designed for sales teams in 2005.

They still look like:

Cluttered interfaces
"Enterprise-ready" features nobody uses
Manual data entry everywhere
No API access to your own data

If you're a developer building a product, why are you using tools that fight you?

What Developers Actually Need

1. API-first
Every feature accessible via REST. No "use the UI or nothing."

2. Custom data models
Not every business is contact-deal-account. Build YOUR schema.

3. AI-native
Agents should handle the ops work: follow-ups, reminders, pipeline updates.

4. No enterprise bloat
Setup in minutes, not weeks. Learn one thing, not fifteen.

The Coherence Philosophy

We built Coherence for exactly this use case:

// Get all contacts
const contacts = await coherence.contacts.list();

// Update pipeline stage
await coherence.contacts.update(id, { 
  stage: 'qualified' 
});

// Set automated follow-up
await coherence.automations.create({
  trigger: 'no_response_7_days',
  action: 'send_sequence',
  sequence: 'follow_up_1'
});

No vendor lock-in. Your data. Your API.

For Founders Building on Ship24

If you're building shipping tracking tools, e-commerce platforms, or any product-focused SaaS, you need a CRM that:

Tracks customer relationships without overhead
Updates automatically as you work
Actually scales with your business

What does your current customer tracking look like?

Would love to hear what tools developers are using for customer relationships. Comment below.

developers #indiehackers #saas #api #bootstrapped

Building a FinTech CRM: Why Your API Stack Matters More Than Features

Keith Fawcett — Sun, 05 Apr 2026 10:00:19 +0000

Building a FinTech CRM: Why Your API Stack Matters More Than Features

When you're building financial products, your CRM isn't just about managing contacts—it's about maintaining trust.

After working with dozens of FinTech companies, here's what separates the CRMs that scale from the ones that become expensive tech debt.

The API-First Reality

Most CRMs treat APIs as an afterthought:

Rate limits that don't match real usage
Webhook reliability that's "eventually consistent"
Documentation that's 3 versions behind

For FinTech, this is unacceptable. Your CRM needs to:

Update in real-time, not batch
Integrate with your payment processor, not just your email
Handle compliance requirements without manual work

What "API-First" Actually Means

It's not just having an API. It's designing your entire workflow around it.

// Real-time pipeline updates
const pipelineUpdate = await coherence.webhooks.create({
  event: 'deal.stage_changed',
  handler: async (data) => {
    // Auto-update compliance docs
    await complianceTracker.update(data);
    // Notify relevant parties
    await notificationService.alert(data);
  }
});

The Integration Stack That Matters

For FinTech companies, your CRM should integrate with:

Payment processors (Stripe, Plaid)
Banking APIs (Mercury, Brex)
Compliance tools (DocuSign, ComplyAdvantage)
Analytics platforms

Not just Salesforce, HubSpot, and Mailchimp.

The Real Test

Ask your CRM vendor this question:

"What happens when my API call fails?"

If they don't have a clear answer with SLAs, run.

The founders who win in FinTech aren't picking the CRM with the most features.

They're picking the one that plays nice with their stack and doesn't require them to babysit it.

What's your current integration pain point? Let's discuss in the comments.

FinTech #API #Development #CRM #Startups

Beyond Autocomplete: How AI Agents Are Transforming Business Operations in 2026

Keith Fawcett — Sun, 05 Apr 2026 01:46:18 +0000

Beyond Autocomplete: How AI Agents Are Transforming Business Operations in 2026

The conversation around AI in software development has shifted dramatically. In 2024, we debated whether AI tools were useful. In 2025, we measured productivity gains. In 2026, we're asking a different question: How do we build systems where AI doesn't just assist—it executes?

This isn't about faster code completion or smarter autocomplete. It's about AI agents that understand context, take autonomous action, and operate within real business workflows.

The Evolution: From Tool to Teammate

The Stack Overflow 2025 Developer Survey told a fascinating story: 84% of developers now use AI tools, but only 29% trust them. That gap between adoption and confidence reveals something critical—developers aren't skeptical of AI's capabilities. They're skeptical of AI's reliability in production systems.

The tools that will win in 2026 aren't the ones with the best demos. They're the ones that:

Take responsibility for outcomes, not just outputs
Operate within existing workflows rather than requiring new ones
Remember context across sessions and compound value over time
Integrate across systems instead of creating new silos

What This Means for Business Infrastructure

At Coherence, we've been thinking about this through the lens of XRM (Extended Relationship Management). Traditional CRMs are database-driven—your team enters data, and the system stores it. AI-powered CRMs are different: they act on that data.

Consider the difference:

Traditional CRM:

Lead fills out form
Sales rep manually creates contact record
Sales rep manually schedules follow-up
Sales rep manually logs meeting notes
Manager manually pulls reports

Agentic XRM:

Lead fills out form → AI creates contact, qualifies lead, assigns to best-fit rep
AI analyzes rep's pattern and lead's attributes → schedules optimal meeting time
AI prepares context brief for the rep before the meeting
Post-meeting → AI logs notes, extracts action items, updates records, triggers follow-ups
Real-time dashboards update automatically with AI-generated insights

The human isn't removed from the process. They're elevated to orchestrator and decision-maker, while AI handles execution.

The Multi-Agent Architecture Behind Modern XRM

Just as microservices replaced monoliths in backend architecture, multi-agent systems are replacing single AI assistants in business operations. At Coherence, we run specialized agents for:

Lead qualification — Analyzing behavioral signals to score and route leads
Meeting intelligence — Preparing reps with relevant context before calls
Follow-up automation — Ensuring no lead goes cold with intelligent outreach
Relationship tracking — Monitoring engagement patterns and flagging at-risk accounts

Each agent is specialized. Each agent shares context with the others. And crucially—each agent operates within guardrails that ensure compliance and data integrity.

The Quality Crisis: Why More AI Isn't Always Better

Here's the uncomfortable truth the industry is grappling with: AI-generated code and AI-generated decisions both have quality problems when deployed without oversight.

The DORA 2025 report found that AI adoption correlates positively with delivery speed and with higher instability. More change failures. More rework. Longer resolution cycles.

This isn't an argument against AI. It's an argument for AI with structure:

Clear success criteria and success metrics
Human oversight at decision points
Feedback loops that improve over time
Integration with existing systems of record

Building AI That Compounds

The tools that win in 2026 won't be the ones with the flashiest demos. They'll be the ones that:

Live inside existing workflows — Not require new ones
Reduce decision burden — Not just save clicks
Earn trust over time — Through consistent, reliable execution
Integrate across systems — Rather than creating new data silos

Software without memory doesn't compound. The XRM of 2026 remembers. It learns. It gets better at serving your customers the longer it runs.

The question isn't whether AI will transform business operations. It's whether your tools will transform—or just look like they did in 2024.

What challenges are you facing as you move from AI experimentation to AI execution in your organization? Share in the comments.

ai #programming #startup #productivity #devtools

Developer CRM Showdown: Coherence vs HubSpot vs Salesforce

Keith Fawcett — Thu, 02 Apr 2026 23:00:05 +0000

Developer CRM Showdown: Coherence vs HubSpot vs Salesforce

Methodology

Tested 5 common developer workflows:

Lead creation via API
Custom data model setup
Webhook integration
AI agent configuration
Pipeline automation

Results

Criteria	Coherence	HubSpot	Salesforce
API-first	✅ Native	⚠️ Limited	❌ Desktop-focused
Webhooks	✅ Real-time	⚠️ 15min delay	❌ Polling only
Custom models	✅ Native	❌ Fixed schema	⚠️ Enterprise only
AI agents	✅ Built-in	⚠️ Add-on	❌ Manual only
Developer docs	✅ Excellent	⚠️ Basic	⚠️ Enterprise docs
Setup time	< 30 min	2-4 hours	1-2 weeks

Verdict

For developer-led teams:

Coherence: Native API, AI-first, fast setup
HubSpot: Good for marketing, limited dev features
Salesforce: Enterprise power, massive overhead

Your CRM should work as hard as you do.

Compare plans →

The Quality Crisis in AI-Generated Everything: Building Systems That Earn Trust

Keith Fawcett — Thu, 02 Apr 2026 16:45:05 +0000

The Quality Crisis in AI-Generated Everything: Building Systems That Earn Trust

Here's what the data actually shows:

84% of developers use AI tools
45% of AI-generated code contains security vulnerabilities
AI adoption correlates with higher instability, not lower
Developer sentiment toward AI tools dropped from 70%+ to 60% in a single year

We're generating more code and more decisions than ever. The quality of both is getting worse.

This isn't a bug in AI. It's a feature of how we're deploying it.

The Problem With "Move Fast and AI Things"

The DORA 2025 report studied nearly 5,000 technology professionals and found something uncomfortable: AI adoption correlates positively with delivery speed and with higher instability. More change failures. More rework. Longer resolution cycles.

This tracks with what many teams are experiencing. AI tools are spectacular at generating code quickly. They're less spectacular at generating code that:

Handles edge cases correctly
Integrates cleanly with existing systems
Scales predictably under load
Doesn't introduce security vulnerabilities
Can be maintained by future engineers (including future AI agents)

The productivity gains are real—studies show 55% faster completion times. But only when paired with rigorous review and testing. Without that oversight, teams report 41% higher code churn and 7.2% decreased delivery stability.

The Three Failure Modes of AI Systems

After working with teams deploying AI across business operations, I've identified three recurring failure modes:

1. The Black Box Problem

AI makes decisions without explainability. Your system recommends routing a high-value lead to a specific rep. Why? "The model said so." When that rep burns out and leaves, you've lost institutional knowledge that was never documented.

2. The Context Collapse Problem

AI operates on the data it can see, not the context it can't. An AI CRM might recommend discounting for a deal that's been in negotiation for months—but it doesn't know the CEO just told the prospect to expect a discount. Now your AI is undermining your negotiation.

3. The Compound Error Problem

Small errors in AI systems don't just stay small. They cascade. A slightly wrong lead score routes to the wrong rep. That rep misses a follow-up because they were overloaded. The prospect goes dark. Three months later, your AI reports "lead quality declining" when the actual problem was a routing error that compounded.

Building AI That Earns Trust

The teams succeeding with AI in 2026 aren't running more AI. They're running structured AI—systems with:

Explicit Success Criteria

Before deploying any AI decision, define what success looks like:

What outcome are you optimizing for?
How will you measure it?
What's the acceptable error rate?
When should the AI defer to a human?

Feedback Loops That Compound

The difference between AI that degrades and AI that improves is feedback. At Coherence, every AI recommendation includes:

The reasoning (explainability)
The confidence level (uncertainty quantification)
The option to override (human-in-the-loop)
Outcome tracking (were we right?)

This data feeds back into model improvement. The system learns from its mistakes—and from the times humans overrode it.

Guardrails That Prevent Cascade

Every AI system needs circuit breakers:

When confidence is low, defer to humans
When outcomes deviate from expectations, flag for review
When errors are detected, document and learn

Integration with Existing Systems

AI that operates in a silo generates decisions based on incomplete data. The AI systems that compound value are the ones integrated with your existing systems of record—the CRM, the support ticket system, the communication history.

The Bottom Line

The quality crisis isn't an argument against AI. It's an argument for AI with architecture.

The tools that will survive 2026 aren't the ones that generate the most outputs. They're the ones that:

Take responsibility for outcomes
Show their work with explainability
Learn from mistakes through feedback loops
Prevent cascade through guardrails
Integrate across systems for complete context

Software without memory doesn't compound. AI without structure doesn't trust.

Build the second kind.

What's your experience with AI quality in production systems? Share your battle stories—and solutions—in the comments.

ai #programming #quality #devtools #startup

Building Your First CRM Stack as a Solo Dev: A Practical Guide

Keith Fawcett — Wed, 01 Apr 2026 17:29:04 +0000

Building Your First CRM Stack as a Solo Dev: A Practical Guide

You're a founder who codes. You've got a product. Now you need to track customers, follow up on deals, and not lose sleep over a spreadsheet breaking at 2 AM.

The honest truth? Most CRM guides are written for sales managers with 10-person teams. Not for you.

The Problem with "Enterprise CRM for Founders"

When you search "CRM for solo founders," you get:

Salesforce tutorials aimed at VP of Sales
Pipedrive training on forecasting for scaling teams
HubSpot "best practices" that assume you have marketing automation needs

None of this applies when you're one person juggling code, customer conversations, and keeping the lights on.

What You Actually Need

As a solo dev-founder, your CRM stack needs exactly three things:

1. A Single Source of Truth for Customer Data
Not a spreadsheet (fragile), not scattered Slack DMs (unscalable), not your email inbox (chaos). One place where every customer interaction, deal status, and note lives—searchable, filterable, automatable.

2. Integrations With Your Actual Tools
Your CRM should connect to Stripe (for subscription data), GitHub (for product usage insights), your email (for conversation history), Slack (for quick lookups), and your code (via API). If it doesn't integrate, it adds friction instead of removing it.

3. Speed, Not Configuration
Enterprise CRM software requires weeks of setup. Your stack should work in hours. You're not configuring workflows for 50 salespeople—you're building a second brain that works how you already think.

A Practical Stack Example

Here's what works for most technical founders:

Customer Data Layer
    ↓
+ Automation Layer (Zapier, Make, or custom scripts)
    ↓
+ Communication Layer (Email, Slack, SMS)
    ↓
+ Analysis Layer (Dashboards, reporting)

For a solo founder, this might look like:

CRM Core: API-first platform that lets you define your own data structure (not forced contact-deal-account schema)
Webhook triggers: When a customer converts in Stripe, auto-create a deal record
Custom fields: Track whatever matters to your business (feature requests, bug reports, business model, etc.)
Email integration: See full conversation history without switching apps
Quick API access: Write scripts to bulk-import customers, export for analysis, or trigger actions

Why This Matters for Revenue

Most solo founders underestimate how much lost revenue comes from bad systems:

You miss follow-ups because the last conversation got buried
You can't segment customers to pitch the next feature to right people
You spend 5 hours building a custom reporting script instead of selling

A proper CRM stack costs $50-500/month and saves 5-10 hours weekly.

That's $200-500 per hour of reclaimed time. Do the math.

The Dev-Friendly Approach

If you're building a CRM stack, look for:

REST API (not RPC, not GraphQL-only—REST is the lingua franca)
Webhooks for event-driven automation
Bulk operations so you can script imports
Custom data models (seriously, why force everyone into contact-deal-account?)
Rate limits that don't punish builders (100 req/sec minimum)
Documentation that assumes you know what you're doing

Real Example: The Notion CRM Pain

Many solo founders start with Notion because it's flexible and free. Six months later:

You can't filter customers by "spent > $5K and no contact in 30 days"
Syncing Notion to Stripe breaks when the API changes
Your "CRM" is 12 interconnected databases that take 20 minutes to query

Then you either:

Hire someone to manage it (defeats the "solo" premise)
Rebuild in a proper tool (sunk cost fallacy)
Go back to spreadsheets (cycle repeats)

A developer-friendly CRM prevents this trap entirely.

What This Means for Your Business

Once you have a working CRM:

Personalization becomes effortless: You remember context about every customer
Follow-up is automatic: Reminders, sequences, and smart routing without manual work
Data lives somewhere queryable: You can actually answer "which customers are most engaged?" in 30 seconds
Revenue compounds: Better customer relationships + faster follow-up = higher retention + better deals

Getting Started

Don't overthink the stack. The best CRM is the one you'll actually use.

Start with:

Define your data model (what do you need to track?)
Pick your core tool (something with API + webhooks)
Connect 2-3 critical integrations (Stripe, email, your main tool)
Run it for 30 days and adjust

Most solo founders find their groove within a week.

The Founder-Dev Advantage

Here's the secret advantage you have that enterprise sales teams don't: you can build custom solutions in an afternoon.

Your CRM can be exactly as simple or complex as you need it. No configuration meetings. No waiting for IT approval. No enterprise bloat.

Use that.

Have a CRM horror story? Or a setup that works great? Drop a comment—I'd love to hear what's working for other solo founders.