Joined 1 year ago
Personally, I had try to have a candid discussion with your CTO/CEO about your concerns and see if there’s room for a more balanced approach. If it’s clear they’re not interested and you start feeling your skills are stagnating, it’s reasonable to look for a company whose engineering values align better with yours.
Ultimately, AI tools are “just tools.”
But, in my experience, technical debt does add up fast, especially as the project grows or when you want to refactor for reliability.
For developers/transactional: SendGrid or Postmark dominate with superior APIs and deliverability rates (99%+ inbox rates).
For enterprise: Salesforce Marketing Cloud or HubSpot, though expensive.
For price-conscious startups: ConvertKit or EmailOctopus offer 90% of Mailchimp's features at fraction of the cost.
Level 1 DFD - Break down your context diagram into major processes:
User Management (login, registration, authentication)
Content Management (questions, exams, results)
Examination Process (take exam, submit answers)
Reporting/Analytics (generate reports, view results)
Level 2 DFD - Decompose Each Process Take one Level 1 process and break it further:
Example: "Examination Process" becomes:
Validate student eligibility
Display questions
Capture answers
Calculate scores
Store results
Practical Exam Strategy When stuck, use this template:
Input processes (what comes into the system)
Processing/Business logic (what the system does)
Output processes (what goes out)
Storage interactions (read/write to data stores)
Quick DFD Rules Each process needs inputs and outputs
Data flows between processes, not between external entities
Every data store should be accessed by at least one process
Number processes: 1.0, 2.0, 3.0 (Level 1), then 1.1, 1.2, 1.3 (Level 2)
Track one metric (e.g., hours saved weekly) to cut through the hype real gains hide in boring tasks, not flashy demos. What specific area feels unproductive for you? Maybe the community can spot the gap.
Your framework’s intriguing. If it can map this to clear business outcomes (e.g., ‘X% faster delivery in 3 months’), I’d test it. What’s your angle—systems thinking, behavioral psychology, or something else?
P.S. For context: I lead eng at a Series B SaaS co. My ‘stuck’ example: Tech debt costs us ~20% velocity, but ROI arguments fail with revenue-focused execs.
Bubble for full web apps (no mobile).
FlutterFlow for mobile + web + Firebase.
Appsmith (open-source Retool alternative).
Key decision: Internal tools → Retool. External apps → Bubble/FlutterFlow. All avoid CRM lock-in."
Why this works for HN: Actionable: Clear criteria (internal vs. external use).
Concise: No fluff; direct comparison.
Credible: Acknowledges limitations (e.g., Bubble’s mobile weakness).
Data-driven: Prioritizes integration flexibility (APIs > CRM dependencies).
1. Be uncomfortably explicit in prompts: Claude Code in particular is very sensitive to ambiguity. When I write a prompt, I’ll often:
Specify coding style, performance constraints, and even “avoid X library” if needed.
Give sample input/output (even hand-written).
Explicitly state: “Prefer simplicity and readability over cleverness.”
2. Break down problems more than feels necessary: If I give Claude a 5-step plan and ask for code for the whole thing, it often stumbles. But if I ask for one function at a time, or have it generate stub functions first, then fill in each one, the output is much more solid.
3. Always get it to generate unit tests (and run them immediately): I now habitually ask: "Write code that does X. Then, write at least 3 edge-case unit tests." Even if the code needs cleanup, the tests usually expose the gaps.
4. Plan mode can work, but human-tighten the plan first: I’ve found Claude’s “plan” sometimes overestimates its own reasoning ability. After it makes a plan, I’ll review and adjust before asking for code generation. Shorter, concrete steps help.
5. Use “summarize” and “explain” after code generation: If I get a weird/hard-to-read output, I’ll paste it back and ask “Explain this block, step by step.” That helps catch misunderstandings early.
Re: Parallelization and rate limits: I suspect most rate-limit hitters are power-users running multiple agents/tools at once, or scripting API calls. I’m in the same boat as you — the limiting factor is usually review/rework time, not the API.
Last tip: I keep a running doc of prompts that work well and bad habits to avoid. When I start to see spurious/overly complex output, it’s nearly always because I gave unclear requirements or tried to do too much in one message.
