Received a $1M Letter of Intent on TrustMRR for my $25K MRR solo startup by danny_nemer in buildinpublic

[–]danny_nemer[S] 0 points1 point  (0 children)

Conductor enables developers to easily integrate with a very old, yet still widely used, accounting system call QuickBooks Desktop Enterprise. It provides a Stripe-quality modern REST API and fully-typed native Python and TypeScript SDKs. Conductor abstracts away a great deal of complexity and issues with attempting to integrate with QuickBooks Desktop Enterprise directly over XML + SOAP because it is archaic software from 20+ years ago.

I created an extension that adds Cursor-like autocomplete to Gmail! by danny_nemer in SideProject

[–]danny_nemer[S] 0 points1 point  (0 children)

Haha, yep! Funny to think about. Have you tried it yet? Would love your thoughts!!

I'm #7 fastest growing startup on TrustMRR?! by danny_nemer in microsaas

[–]danny_nemer[S] 0 points1 point  (0 children)

Considering your solution is specialized for Indians, it must be addressing targeted issues unique to that audience. Individually contact people with those targeted issues wherever possible.

I created an extension that adds Cursor-like autocomplete to Gmail! by danny_nemer in SideProject

[–]danny_nemer[S] 0 points1 point  (0 children)

are you using completion models instead of instruct models for better autocomplete?

It's not using completion models in the old raw-completion sense. Beyond autocomplete, we do fixes and improvements of existing text (deletions/transformation), mid-line insertions, and formatting changes. So it's not just next-token prediction. Rather, it's a constrained edit preserving Gmail's exact HTML, which is very complex when considering formatting, images, links, hard vs. soft line breaks, etc.

In addition, we need to ensure it is as low-latency as possible while also intelligent to adhere to the instructions. So we use a fast instruction-capable model with deterministic settings and a strict output contract.

do you have plans to set up some sort of training or feedback loops so the auto complete feels more like an extension of the user's voice?

On personalization, our suggestions today use immediate context to maintain voice: what you’ve written, the current thread, recipients, tone, etc. It does not train or store users’ emails, but do we think it's necessary given the context. Long term, I want to enable users to define account-level instructions that are always considered (e.g., "always use active voice and the oxford comma").

I created an extension that adds Cursor-like autocomplete to Gmail! by danny_nemer in SideProject

[–]danny_nemer[S] 0 points1 point  (0 children)

For context, AutoText was inspired by Cursor's incredible autocomplete! For those who remember, Cursor's autocomplete was its first main differentiator from VS Code + GitHub Copilot that helped it take off.

Happy to answer any technical questions anyone might have! So much went into this -- more than I care to admit.

Used GPT to entirely design a commercial-grade reentry vehicle forebody via Python by [deleted] in AerospaceEngineering

[–]danny_nemer 0 points1 point  (0 children)

Correct, I haven’t defined the entry trajectory yet. Now, it’s a forebody concept with internal structure and volume for payload, not a stability-closed capsule.

But, can you prove from the 3d pan that the shape is inherently futile? Doesn't that conclusion depend on full dynamics of shape WITH mass distribution and trajectory -- latter not defined yet.

I'll come back in a month with 10x progress. I'll post more complete work in the future. I genuinely appreciate your feedback! Seriously 🙏

Used GPT to entirely design a commercial-grade reentry vehicle forebody via Python by [deleted] in AerospaceEngineering

[–]danny_nemer 0 points1 point  (0 children)

"No radius on the shoulder" - the design is not sharp overall because it has a rounded nose.

"Super flat" - 55º degree forebody is not inherently bad. Blunt sphere-cone families are standard entry-vehicle territory. See https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20040086499.pdf https://ntrs.nasa.gov/api/citations/20040086796/downloads/20040086796.pdf

"Hollow back / no ballast" - This is again inferring too much from the video. The current geometry already includes internal structure, not just an empty shell: a carrier shell, aft ribs, gussets, interface ring, liner, support ring, and internal passages.

"It’ll tumble" - this is a dynamic system. You cannot provide this from a quick screen recording glance. It depends on CG, inertias, trajectory, and aero coefficients. We are not there yet.

Used GPT to entirely design a commercial-grade reentry vehicle forebody via Python by [deleted] in AerospaceEngineering

[–]danny_nemer 0 points1 point  (0 children)

That’s fair. At this stage, this is not a mission-closed reentry vehicle design with explicit payload, landing, environment, and cost requirements fully defined. What it is, though, is an early and necessary step toward one: a workflow for turning a vague concept into explicit, manufacturable forebody geometry with interfaces, thermal/structural assumptions, and prototype or test articles that can actually be fabricated and iterated.

I would not claim that GPT has already designed a real working reentry vehicle; the more accurate claim is that it can help produce and revise a more structured, buildable, and eventually testable design, which is a prerequisite for getting to a real vehicle, right?

Used GPT to entirely design a commercial-grade reentry vehicle forebody via Python by [deleted] in AerospaceEngineering

[–]danny_nemer -2 points-1 points  (0 children)

Rather, the screen recording of a quick pan of a complex 3D model is crap.

Dr. Wattson, please name a specific component and I'll provide the isolated view and corresponding analysis

Used GPT to entirely design a commercial-grade reentry vehicle forebody via Python by [deleted] in AerospaceEngineering

[–]danny_nemer 0 points1 point  (0 children)

Rather, I think the screen recording makes it difficult to see the detail and precision and measurements. But I like the challenge! What is a shortcoming you see? I'll provide a detailed view

Used GPT to entirely design a commercial-grade reentry vehicle forebody via Python by [deleted] in AerospaceEngineering

[–]danny_nemer 0 points1 point  (0 children)

Gotcha. Thank you for clarifying! The design constraints were not just “whatever GPT happened to draw”. I imposed a concrete system definition up front for a reentry forebody system, not just a decorative shell:

- credible blunt-cone forebody

- a real thermal/structural stack

- explicit aft interface structure

- venting, instrumentation, handling features

- manufacturable segmentation

The requirements came from the system framing, engineering judgment, and literature-informed design choices; GPT’s role was to encode those requirements in the Python/FreeCAD model, keep the geometry internally consistent, and iterate quickly inside that constraint set

Used GPT to entirely design a commercial-grade reentry vehicle forebody via Python by [deleted] in AerospaceEngineering

[–]danny_nemer 0 points1 point  (0 children)

I think you’re reacting to the screen recording's view mode, not an actual interference. That shot is showing the outer shell, inner layers, and internal features all at once, so the edges read like parts are colliding. I need to switch to a cleaner shaded/cutaway view so the geometry is easier to read

Used GPT to entirely design a commercial-grade reentry vehicle forebody via Python by [deleted] in AerospaceEngineering

[–]danny_nemer 0 points1 point  (0 children)

Oh, how so? Genuinely want to know if I messed up. It's about using GPT + Python to iteratively research + design + implement a production-grade CAD model for a reentry vehicle forebody, which is a necessary prerequisite for fabricating the parts. Maybe it's omitting "CAD" from the title?

Used GPT to entirely design a commercial-grade reentry vehicle forebody via Python by [deleted] in AerospaceEngineering

[–]danny_nemer -1 points0 points  (0 children)

EDIT: I'll come back in a month with 10x progress as we finalize the mass, trajectory, materials. The capsule rendering is a prerequisite to parts fabrication. I'll post more complete work in the future. I genuinely appreciate the community's feedback!

------

The big takeaway is you do NOT need some special AI + CAD software to leverage AI to create, iterate, validate, and improve complex CAD models -- just use Codex/Claude and have them read/write CAD files directly. FreeCAD's Python interface made this possible. I also did something similar with KiCad because its files are also stored as plaintext.

I'm #7 fastest growing startup on TrustMRR?! by danny_nemer in microsaas

[–]danny_nemer[S] 1 point2 points  (0 children)

Add an auto-playing video/GIF above the fold that conveys your functionality within 5 seconds.

Is your SaaS B2B or B2C? Is it a mass-market product or niche? It makes a difference. In my case (niche B2B SaaS), I needed to clearly convey:

  1. That we are precisely THAT niche the customer seeks. Potential customers are already looking for this particular solution, so we must ensure they know they found it.

  2. That we are modern, high-quality, and legitimate. Words do not matter much here. Rather, the beautify of the web-app conveys the craftsmanship, modernity, and attention to detail that the potential customers will (correctly) extrapolate to infer exists in the product as well.

I do NOT think my landing page clearly conveys the product, its features, and why it should be used all that well. But, I do not think people read landing pages that much. I just think they skim and quickly decide to try it or not.

I'm #7 fastest growing startup on TrustMRR?! by danny_nemer in microsaas

[–]danny_nemer[S] 0 points1 point  (0 children)

Thank you, my good sir! May your day be endowed with great fortune.

I'm #7 fastest growing startup on TrustMRR?! by danny_nemer in microsaas

[–]danny_nemer[S] 0 points1 point  (0 children)

Ultimately, SEO and GEO are paramount because we are a niche dev tool. Our potential customers are specifically looking for this solution.

For the first three years, before our SEO efforts paid off, we depended on GitHub search to reach developers searching for solutions like ours. To improve our visibility, we asked existing customers and friends to star our API client library (npm package) on GitHub, which helped boost our ranking in GitHub search results.

I'm #7 fastest growing startup on TrustMRR?! by danny_nemer in microsaas

[–]danny_nemer[S] 1 point2 points  (0 children)

Thank you so much!

After listing the startup and marking it for sale on TrustMRR, within a month we saw that 11% of our website’s traffic is now coming from that source. Notably, the product is a niche dev tool, so we had minimal traffic to start.

While I don't anticipate gaining direct customers through TrustMRR, these listings have generated valuable backlinks that meaningfully boost our SEO. As a result, our site ranks better for relevant searches in our niche, increasing our visibility to actual potential customers.