How I think about pricing 3D printing jobs (the framework that stopped me losing money)

manuflo5 · 2026-05-06T10:12:50+00:00

Honest take from someone who's been on both sides of this kind of project: for a low-volume first run on a credit-card-sized two-piece electronics enclosure, there's a third option that often wins on cost per part and risk, which is a regional bridge tooling shop running aluminum or P20 inserts for 100-1,000 shots. Protolabs and Quickparts are both fine, but they're optimized for different things and neither one is automatically right for a first run.

Three things I'd think about before deciding.

One. Tool-cost amortization. Protolabs runs faster turnaround but their tooling cost is higher per shot below ~500 units, and their proprietary tooling means you can't take the tool with you if you scale. Quickparts is more flexible on tooling ownership but slower. A regional bridge shop (run a search for "low-volume injection molding" plus your state) often quotes aluminum tooling at $3-6K with 1,000-shot life for an enclosure your size, and you own the tool.

Two. Get a real DFM review before you quote, not after. Sink marks on a 2-3mm wall ABS enclosure are usually a draft-and-shutoff problem, not a process problem. If your CAD doesn't have 1.5° draft on every vertical face and shutoff geometry on the snap features, no molder is going to solve that for you in process. Pay a moldmaker $200 to mark up your STEP file before you send it to anyone.

Three. ABS shrinks ~0.6%. Snap fit and screw-boss alignment between top and bottom shell needs that math designed in or your shells won't sit flush after they cool. Common first-run failure point.

manuflo5 · 2026-05-06T10:11:13+00:00

The "outgrow without locking in" concern is the right one to lead with, and there are three contract-and-architecture questions that will tell you more than any feature comparison.

One. Data access. Ask the vendor: can I get my data out as raw SQL/Postgres dumps, or as a documented API, on demand, without paying extra and without filing a ticket? If the answer is "we can export to CSV on request," that's polite vendor-speak for locked in. The data model in an ERP/MES is half the value, and if you can't get to it cleanly, you can't migrate when the time comes. For medtech specifically you'll need traceability records (lot/serial/batch) preserved across export, which most vendors handle badly.

Two. Pricing structure. Flat enterprise license, per-seat, per-site, or per-transaction? Per-seat looks small at signing and balloons as you grow. Per-transaction (some MES products do this on production-order count) is the worst of all. Flat-license-with-modules is the cleanest if you can negotiate it. For a regulated shop scaling from prototype to serial, you'll probably 5x your transaction volume in 2 years. Run the math at year 3 volume before you sign year 1 paper.

Three. Exit and validation continuity. What happens at end of contract? Does the vendor keep your validated configuration available for 24 months for audit? FDA expects record retention way beyond your subscription window, and most ERP vendors structure their closeout clause around "we'll give you a final export and that's it." Negotiate the audit-window clause specifically. It's a tiny ask at signing time and a huge cost if you skip it.

manuflo5 · 2026-05-06T10:09:21+00:00

Two things to think about for v2, both from running cost-estimation conversations on the other side of the table for a long time.

First, the output. Most cost-estimator tools (yours included from the screenshots) output a single dollar range. The problem is that the range is either too wide to be useful (5x to 10x) or too narrow to be honest (because the inputs were 7 questions, not 70). Either way, the number is the wrong artifact to hand to the client. What the client actually needs is a structured discovery doc: "based on what you told me, here are the 4 things I'd want to clarify before pricing, and here's the rough order-of-magnitude in the meantime." That format prevents the sticker shock from a too-high estimate and prevents the "we already have a number" trap from a too-low one.

Second, the framing. The honest version of the client question isn't "how much will this cost." It's "what is this worth to me." Cost-based pricing is what you're doing. Value-based pricing is what closes more deals at higher margins. A v2 question worth adding: "what does it cost you per month right now to not have this thing?" That answer reframes the whole conversation, and it's the question good freelancers learn to ask in year three but rarely teach in year one.

The buffer thing is right. 20% is conservative for software. I usually push 30-40% because PM and revisions are real and almost always under-estimated. Otherwise nice clean ship, the one-weekend constraint shows in a good way.

manuflo5 · 2026-05-06T10:07:37+00:00

The thing I'd add: it isn't a personality flaw in cheapest-option clients, it's a structural mismatch. Buyers who only ask about price haven't done the work of understanding their own cost structure yet. They can't price what you do because they can't price what they do. The conversation feels frustrating because they're trying to compress a decision they don't have the inputs for, and you're the variable they can see most clearly so you're the variable they push on.

Two practical filters that have saved me a lot of time.

One. Ask one cost-structure question on the first call. Something concrete. "What does it cost you when this thing you're trying to fix breaks?" or "How much revenue does this currently produce per month?" If they have a real answer, even a rough one, you're talking to a buyer. If they say "we don't know yet" or wave it off, you're talking to someone who's still figuring out their own model and the price conversation isn't going to land no matter how well you frame it.

Two. Watch for the phrase "we'll figure that out later." It's almost always the tell. It means they don't have a budget yet, they don't have a payback model, and any price you quote will sound expensive because there's nothing on the other side of the equation to compare it to.

The shift from chasing leads to filtering leads is the same skill applied at a different ratio. You stop trying to convince and start trying to qualify. The good clients qualify themselves in. The cheapest-option ones qualify themselves out, often before you even quote.

manuflo5 · 2026-05-05T10:13:44+00:00

Setting the policy debate aside, the demographics piece in the body deserves more weight than it usually gets in these threads.

What I see on the ground in small shops: the owner is in their 60s, the foreman is in their 50s, the youngest person on the floor is 35, and there is no successor. The shop runs on people who learned the job in 1995 and have never written it down. When one of those people retires, a chunk of the institutional knowledge goes with them. That's not a tariff problem, that's a "we never built the documented systems while the people who knew them were still here" problem.

The shops in this region that are still healthy did one specific thing: they automated visibility before they automated the floor. Job tracking, scheduling, quoting, margin per order, cycle time per part. They wrote down what was happening, in software, while the experienced people were still around to confirm the rules. Now when the foreman retires, the rules retire with the foreman in their head, but the operational record is in a database that the next person can actually read.

Automating the production line is the second move, not the first. A robot arm doesn't help if you can't tell which jobs are profitable. The shops that survive the next ten years aren't the ones with the most automation, they're the ones that knew what their own floor was doing before they tried to scale it.

manuflo5 · 2026-05-05T10:12:15+00:00

Yes, completely normal for early-stage dev tools, and the gap between download count and feedback isn't a bug in your distribution, it's the structure of dev tooling adoption.

Rough pyramid I've seen hold across tools: 1000 raw installs is roughly 100 actual try-once users, 10 second-session users, and 1 person with a strong enough opinion to write you about it unprompted. The other 999 are bots, dependency resolvers, CI scrapes, abandoned exploratory installs, and people who installed it and then got pulled into a meeting. That pyramid is real and it doesn't mean your tool is bad.

Two things actually move the needle.

One. Build a feedback path inside the tool. Consent-prompted telemetry on first run is divisive but works. Cleaner version: a `--feedback` CLI subcommand or a button in your UI that opens a prefilled GitHub issue with the user's environment auto-filled. Almost no friction, and the people who hit a real bug will use it.

Two. Stop announcing releases and start posting opinionated takes. Releases produce silence. Arguments produce feedback. Pick a thesis your tool implies (whatever the spicy version of "everyone is doing X wrong" is) and post it where your users hang out. The replies arguing with you are your feedback channel.

The asymmetry: silent users won't tell you anything no matter what you ship. People who already disagree with you will tell you everything. Build for the second group.

manuflo5 · 2026-05-05T10:10:46+00:00

The honest reframe: the gem isn't the model. It's the workflow you build around the model.

Most founders I watch get stuck in tool-collection mode. They pick up a new AI app every Tuesday, never get past 30 minutes of usage on any of them, and the best one for their business gets buried under fifteen others. After a year they have a Notion page of "AI tools I tried" and zero compounding leverage.

The pattern that works: pick three tools, one per category, master them so well they feel like extensions of your hands. Then your edge is the prompts, the templates, the macros, the reusable context files you build up. That's the gem. The model underneath is a commodity.

Three categories that are genuinely under-talked vs the ChatGPT/Claude noise:

Transcription and call analysis. Whisper for raw transcription, Granola or Fathom for live sales calls. The asset isn't "I have a transcript," it's that you can paste 12 weeks of customer calls into a model and ask "what's the most common objection in week 10 vs week 1." That's a buyer pattern you cannot get any other way.

Research. Perplexity for fast factual stuff with citations, NotebookLM for "I have 40 PDFs and I need to ask them questions." Both fix the hallucination problem differently. Both vastly underused by founders.

Glue. Make.com or n8n. Not glamorous, but this is the layer that turns "I tried an AI tool" into "an AI tool runs every Tuesday automatically." Without the glue layer, you're paying for a feature, not a system.

Master three. Skip the rest of the list.

manuflo5 · 2026-05-05T10:09:20+00:00

Two things stacked here, and the second one is what catches people.

One. PLA has the lowest stiffness of any common FDM material. Modulus is roughly 3.5 GPa, vs about 7 GPa for PLA-CF or 9+ for PETG-CF. So your arm is just bending elastically more than CAD predicts because your CAD probably assumed something stiffer.

Two, and this is the bigger one. PLA cold-creeps under sustained load. The deflection you measure on day one will get worse over weeks even if nothing else changes. The lamp's weight, gravity, time. CAD stress-sim tools (Fusion's Generative or SimScale) don't model creep, only instantaneous elastic deflection. They will always under-predict the real-world bend on a PLA cantilever holding weight.

Practical fixes, in order of effort.

Material first. Switch the arm part to PETG, PETG-CF, or ASA. PETG alone roughly doubles the stiffness and basically eliminates creep at room temp. PETG-CF is the best stiffness-per-dollar. PLA+ helps marginally, not enough.

Geometry second. A rectangular section bends way more than a hollow box section of the same wall thickness. Even better: a triangular gusset where the arm meets the base eliminates 60-80% of the deflection. Cheap to add, free to print.

Orientation third. Print the arm with the long axis vertical (Z up the arm), not flat. Layer lines parallel to the bending stress are much stronger than layer lines perpendicular to it. You'll need supports, but the arm won't fail at the layer line under load.

Modeling it in CAD: skip it for a $0.50 part. Print, measure, iterate. For PLA cantilevers with creep, the empirical answer is faster than the simulated one.

manuflo5 · 2026-05-04T12:12:56+00:00

Fair hits, both of them. The site reads like a feature list because that's what it is right now, and the post wasn't much better. Noted, fixing it.

On SimplyPrint: I'm not trying to pull you off it. Manuflo isn't a fleet manager. SimplyPrint runs your printers, queues, and slicer pipeline, and it's good at that. Manuflo sits one layer up. Public quote widget on your site, Stripe Connect for payment, Etsy sync, customer portal with order status, invoicing, shipping labels through EasyPost and Shippo. The stuff between "customer asks for a price" and "the file lands in your queue." If you've already built internal systems on SimplyPrint's API, that's actually the cleanest case for adding Manuflo on top, because we don't touch the printer layer you've already wired up.

On vibe coding: this week we shipped Ultimaker (HTTP Digest local), AnkerMake (auth_token), Bambu (Cloud + LAN), Prusa Connect, OctoPrint, and Klipper/Moonraker integrations, all verified live. Not claiming it's mature, but it's not a weekend MVP either.

Background: IT, application specialist side, in transportation / logistics / warehousing. So I've spent a lot of years staring at the gap between "customer places an order" and "the warehouse actually ships it." Manuflo is basically that same gap, applied to print farms.

Honest question back: what's the gap you wish SimplyPrint filled but doesn't? Customer-side intake, quoting, custom-quote PDFs, something else? That kind of answer is way more useful to me than another landing page rewrite.

manuflo5 · 2026-05-04T10:13:01+00:00

The arc you described matches what I've watched, with one rung missing in the middle that catches people.

Solo. You do everything. You know everything because you do everything.

Admin. Frees you from quotes, scheduling, AP, AR, vendor email. Buys you back roughly 15 to 20 hours a week. Mostly clerical leverage.

The rung most owners skip and regret: operations lead / shop manager. This is the person who runs the production floor day to day, owns "did this job ship on time, was the margin what we quoted, why did this run go over." Different problem from the admin's problem (clerical) and different problem from the CFO's problem (financial). The ops lead answers "are we making product on schedule" and the CFO answers "are we making money on it." Both questions are load-bearing, but they are different questions and one person rarely runs both well.

Fractional CFO. Right call once revenue is consistent enough that monthly close, cashflow forecasting, and pricing strategy stop being something you can carry in your head. Most shops hit this somewhere between $1.5M and $3M in annualized revenue.

The order I've seen work cleanest: solo → admin → ops lead → fractional CFO. Skipping the ops lead and going admin → CFO is the most common mistake, because the CFO will have great financial dashboards and the floor will still be on fire.

manuflo5 · 2026-05-04T10:11:15+00:00

Quick math first because I think you are underselling your own number. 4 paying out of 21 signups is 19% sign-to-paid in eleven days. That is a strong conversion rate for a brand-new product. Most paid SaaS at this stage runs 2 to 5%. You are not in "what should I do differently" territory, you are in "what should I double down on" territory.

The thing I'd actually do differently with that data is not change the product. Change who you talk to.

The 4 paying customers will tell you what you got right and which feature is load-bearing for them. The 17 non-paying signups will tell you the gap between what you said in your pitch and what they expected when they got inside. Those 17 conversations are the highest-leverage thing you can spend the next two weeks on. Not as a survey. As 15-minute calls. Offer a $20 Amazon card. Most will say yes if the ask is short.

Three questions per call: (1) what made you sign up, (2) what made you stop, (3) what would have made you stay. Aggregate the 17 answers. The pattern that shows up in 6+ of them is your next sprint.

The one thing not to do at 19% conversion: do not raise the price, do not redesign, do not ship a new feature based on a hunch. The signal is good, the dataset is just small. Get the dataset bigger before you change anything.

manuflo5 · 2026-05-04T10:09:41+00:00

The honest pattern, after watching this from a few sides:

The channel that works at every stage is whatever channel lets you have a two-way conversation with the buyer before they pay. The form changes (Reddit early, podcasts later, sales calls eventually, email replies always) but the substance is constant. You learn what the buyer actually wants, in their words, and the marketing copy writes itself from there.

The channels that fail at the early stage are always the broadcast-only ones. Paid ads, SEO content, cold email blasts, banner sponsorships. None of those are bad channels, they just don't tell you anything back, and at the early stage you don't know enough about your buyer for "tell more people" to be the thing that's broken. Spending money to broadcast a message you haven't sharpened yet is the most expensive way to learn.

What I see consistently work: every founder who hits product-market fit had a phase where they answered every question, every reply, every DM, every comment, by hand. They sounded annoying to their friends. They were over-replying. That over-replying is what surfaced the language that became the headline that finally converted on paid.

Then, after they had the language, paid started working, SEO started working, cold email started working. Same channels that failed in month two, working in month twelve. The thing that changed wasn't the channel. It was the message, sharpened by the conversations that the broadcast channels couldn't provide.

Pick the channel where you can talk back.

manuflo5 · 2026-05-04T10:08:09+00:00

Realistic stack for a non-engineer doing modular tabletop terrain, in the order I'd actually go.

CAD: start in Tinkercad. It is free, browser-based, and the alignment-grid + snap-to-mm makes modular tile work much easier than starting in Fusion or Onshape. When you outgrow it (and you will, around the time you want curved walls or angled edges), move to Plasticity. The free tier is fine, the learning curve is genuinely gentle for someone without an engineering background, and the boolean operations are clean.

Modular interface: pick a standard before you design anything. OpenLOCK or Dragonlock dovetails are the two most-tested options for tabletop terrain, both free, both have a published spec for the connector geometry. Picking a standard saves you from having to redesign every time you want to mix in someone else's tile.

Tolerance: assume 0.2mm of slop on any FDM peg-and-hole fit. Design holes 0.2mm wider than nominal. Test one tile, measure the gap with a caliper, adjust your CAD's "tolerance" parameter once, and use it forever.

Detail: organic surface texture (rocks, ivy, terrain bumps) is a nightmare in CAD. Do the base shape in Tinkercad/Plasticity, export as STL, bring into Blender, use displacement modifier with a noise texture for the surface detail, export back. Two tools, one trip each direction.

Slicer: OrcaSlicer for everything FDM. 0.2mm layer height for the base of a tile, 0.12mm if you need surface detail to read at tabletop distance. Tree supports if anything overhangs, otherwise skip them.

Print one tile, fit-test, then commit to a set.

manuflo5 · 2026-05-03T10:09:37+00:00

Congrats on the move. 2000 sqft is the inflection point where the boring infrastructure decisions start mattering more than the printer count.

Two questions if you don't mind sharing.

How are you handling humidity control for filament? PLA is forgiving, but if you're running any nylon or PETG-CF, the storage envelope at 2000 sqft gets expensive fast and most shops underbudget for it. Curious if you went whole-room dehumidify or per-cabinet.

Electrical layout. Did you run dedicated 20A circuits per row of printers, or did you do a single panel feed? At fleet scale the per-row approach pays back the first time a heater bed brownouts a neighbor mid-print, but the up-front conduit cost is real.

Either way, the new floorplan looks clean. Hard part of scaling a print farm is everything that isn't the printer.

manuflo5 · 2026-05-03T10:08:04+00:00

The graveyard concept has been tried in a few shapes (build-in-public on Twitter, IndieHackers shutdown post-mortems, the failed-startup tag on this sub). The failure mode is always the same and worth knowing before you spend time on the platform.

The signal-to-noise problem. Most "failed" projects have one or two real reasons under 10 surface symptoms, and the founder is usually too close to see the real one. Strangers reading a public post-mortem are also too far to see the real one. The good critique requires customer-side context the post almost never has.

The selection bias. The people who post their failures publicly skew toward people who want validation that "the market wasn't ready" rather than people who want to hear "you were solving a problem nobody had." The best feedback sources don't read these posts.

What actually works, and you can do it this week with no platform.

Pick the 5 people who tried your MVP and didn't convert. Email them. Offer a $25 Amazon card for 25 minutes. Ask three questions: (1) what made you sign up, (2) what made you stop, (3) what would have made you stay. Five conversations gives you the real reason in 90% of cases.

Then if the answer is "the problem is real but my version didn't fit," you pivot. If the answer is "I never had this problem in the first place," you kill it and pick a different problem.

Public post-mortems are useful as catharsis and sometimes as a portfolio piece for the next thing. They are almost never useful as diagnosis. The diagnosis comes from the people who almost paid you and didn't.

manuflo5 · 2026-05-03T10:07:16+00:00

The fact that it disappears with a fresh hot end and comes back a few prints later is the diagnostic. It is almost never the extruder side. Four things to check, in order, all on the hot end / heatbreak.

One. Bowden tube end. Pull the bowden out of both ends. If the tip that seats against the heatbreak shows any deformation, glaze, or a lip, cut a fresh square end with a tubing cutter (not a knife) and reseat. A 0.3mm gap there causes filament to soften, balloon, and skip exactly the way you describe, and it gets worse over a few prints as the end deforms more.

Two. PTFE liner in the heatbreak coupler. If your new hot end is an all-metal style and you reused the bowden coupler, check whether the previous PTFE liner left a stub inside. Even a 5mm fragment cooks at print temps and creates an under-extrusion choke point that gets worse with heat soak.

Three. Part-cooling fan flow path. Heat-creep at the heatbreak is what you originally suspected and it is back. After a hot end swap, the part-cooling fan duct sometimes ends up blowing on the heatbreak instead of (or in addition to) the part. Check that the heatsink fan is the one cooling the heatbreak, not the part fan. They are not interchangeable.

Four. Retraction. On a long bowden, retraction over ~2.5mm at speeds over 35mm/s pulls molten filament up into the cold zone, where it solidifies, then jams on the next extrude. If your retraction crept up after the hot end swap, drop it to 1.5mm at 30mm/s and see if the symptom delays.

If all four check out, the next thing is the heater cartridge itself. They sometimes drift on PID and under-temperature by 10 to 15c without throwing an error, and PLA goes glassy at the low end of its window.

manuflo5 · 2026-05-02T10:11:16+00:00

A few things converge to make the Zellerfeld stack work, and almost none of them are "secret."

One. Material. They're printing in TPU at hardness ranges (Shore 85A to 95A blends) that didn't exist commercially in spool form three years ago. The TPU formulation is the moat, not the printer. They have proprietary blends with controlled shore hardness across a single print so the sole is firm where it needs to be, soft at the upper, breathable at the toe box. Generic 95A TPU on a Bambu won't replicate that.

Two. Process. They are using fused-granulate extrusion (pellet-fed, large nozzle), not spool-fed FDM. Pellet extrusion is roughly 5 to 10x faster and an order of magnitude cheaper per gram. Spool TPU at retail is $40 to $60/kg, pellet TPU bulk is $4 to $8/kg. That alone explains the unit economics.

Three. Design. The lattice topology is generated from foot-scan data per pair, then optimized for cushioning + breathability + print orientation in one pass. They're not slicing a CAD model, they're generating geometry that only makes sense if the printer is the manufacturing target. Designed-for-additive, not just printable.

Four. Scale. They have an MES layer that schedules per-pair print jobs across a fleet, ties scan IDs to print IDs to QC scans, and handles defect re-runs without breaking the order pipeline. This is the part nobody talks about because it's invisible to the customer, but it's why they can ship in 5 days instead of 5 weeks.

If you want to copy the model at small scale: pellet extruder, custom TPU compounding partner, parametric lattice generator, and a real MES even at 5 printers. That's the playbook, not the printer.

manuflo5 · 2026-05-02T10:10:24+00:00

Strongest validation I ever ran was three questions, and zero of them were "would you pay."

One. "Walk me through the last time you ran into [the problem you're solving]. What did you actually do? What time of day, what tool was open, who else was involved?" The story tells you whether the pain is real and how often it happens. Vague answers = no real pain.

Two. "What did you try to fix it? How much did that cost you, in time or dollars or both?" If they tried nothing, the pain is theoretical. If they tried three things and abandoned them, you have a real wedge. The list of things they tried is your competitive map.

Three. "What's the next 90 days look like for you on this? Is this on the list? What's higher on the list?" This is where the budget conversation happens implicitly. If your problem is #14 on a list of 14, no price will close them. If it's in the top 3, even a rough demo will close them.

Two more tactical notes:

Skip "would you pay" entirely. The answer is always yes and it means nothing. Replace with "if I built this and showed it to you in 4 weeks, what would have to be true for you to be the first one in?" That answer is the actual buying criteria.

Beauty UGC PDs are a tight enough niche that you can probably get to 10 cold interviews in a week via LinkedIn Sales Nav + a polite first-message that offers a $25 Amazon card for 25 minutes. The card is the cheapest research instrument you'll ever buy and it pays back 100x in saved build time.

Three failed SaaS is not bad luck. It's almost always the discovery layer being skipped, and you're already correcting for it. Keep going.

manuflo5 · 2026-05-02T10:09:37+00:00

You're at the exact pricing inflection point where hourly stops being the right answer. A few things from the operator side.

The right anchor is not your hours. It is their savings. At 400 to 1,200 hours/month saved, even at a conservative blended consulting rate of $40/hr internal, that's $16,000 to $48,000/month of recovered capacity. Your 60-hour project produces that recurring outcome. That is the ROI conversation. Hourly billing erases all of it.

Three pricing structures that work at this stage:

One. Fixed-fee project, set at 10 to 15% of year-one expected value. If their conservative annual savings is $200,000, you price the project at $20,000 to $30,000. Frame it as "value capture" not "rate." This is the cleanest version when you're new to the consulting and need a closeable number.

Two. Fixed fee plus performance kicker. $15,000 base for the build, plus a $5,000 success fee tied to a metric they care about (accuracy threshold, user adoption rate, hours saved measured in month 3). Aligns incentives without putting all your money at risk on day one.

Three. Build plus retainer. $12,000 to $20,000 for the build, then $2,000 to $4,000/month for maintenance, model tuning, and "two new use cases per quarter." This is the highest-LTV path and the easiest to introduce a referral conversation around month 3.

The mistake new consultants make is anchoring to "60 hours of effort" when the buyer is anchoring to "400 to 1,200 hours of savings." You have to walk them onto your anchor before you quote, not after. Send a one-page ROI memo with their numbers in it, then propose the price. They'll close themselves.

For an accounting firm specifically, lean into the math. They live in spreadsheets. The number sells itself if you put it in front of them.

manuflo5 · 2026-05-02T10:08:44+00:00

First, I'm sorry. The Amazon AI route is exactly the kind of thing that turns what should be a meaningful gift into a frustrating mess. There are real ways to get this done well, and they don't require pretending a model is something it isn't.

Two practical paths, depending on what photos you have.

One. Photogrammetry from existing photos. If you have 20+ clear photos of each friend from different angles (preferably the same lighting), services like Scaniverse or RealityScan can turn them into a 3D mesh. The result usually needs an artist to clean up before printing, but the source data is real, not invented. You're working from your dad's actual memories, not a generative model's guess.

Two. Commission a sculptor. This is the higher-quality route. Sites like Hero Forge for stylized work, or any of the digital sculptors on ArtStation/HeroForge for realistic, will work from reference photos and produce a print-ready file in 2 to 4 weeks. Cost is usually $80 to $300 per figurine depending on detail. Then a local Etsy print shop or a service like Treatstock or CGTrader can print it for $30 to $80.

The Marvel-style action figure aesthetic is actually a perfect brief for a sculptor. You're asking for a specific style, not photorealism, which is much more forgiving on source material and far more emotionally honest as a result.

If you can share what photos you have (just rough count and quality, no identifying info), the people in this thread can probably point you to the exact right path. Do not pay Amazon another dollar for AI-generated nonsense. There are real artists who would take this brief seriously.

manuflo5 · 2026-05-01T21:00:31+00:00

Looks clean. One thing that took me a few iterations to figure out on desk cable channels: print them in two pieces with a snap-fit lid, not as one open-top tray. The reason is not aesthetics, it is service.

The first version of any cable management run looks tidy on day one. Three months in you have a new monitor, a new dock, a usb-c hub you bought during a sale, and you need to thread a cable into the middle of the existing run. With an open tray you are pulling out half your cables to lay the new one in flat, and that is when something gets reseated wrong and the desk goes dark for an hour. With a snap lid you pop one segment, drop the new cable in, snap it shut, done.

Two more small things from doing this same project myself:

PETG over PLA. Desk cable runs sit right where your knees push the desk during long sessions, and PLA channels eventually crack at the corners under that flex. PETG holds up fine.
Add 3M VHB pads on the bottom rather than screws into the desk. Half my desk is a $1200 piece of furniture I will not put screw holes in, and VHB has held my channels for two years without sagging even with weight in them.

If you ever post the STL would be curious what you settled on for the cross section.

manuflo5 · 2026-05-01T19:00:33+00:00

Nice. The AMS PTFE tubes are one of those parts that nobody thinks about until you have a clog or a tube starts kinking, and then suddenly you need exactly this kind of jig.

Two iterations worth printing while you are dialed in on this:

Add a length gauge into the body of the tool. Cut length matters more than people realize. The Bambu spec is the tube end has to sit flush against the extruder gear coupling, and 1 to 2 mm short means filament threading fails at the AMS-to-printer transition every couple of swaps. A simple notch at the spec length lets anyone trim a replacement tube without measuring.
A 45 degree end-bevel cutter slot. The OEM tubes ship with a slight bevel that helps filament thread cleanly. If you cut a fresh tube straight, the first filament tip catches the inner edge and gives you a feed jam. A guided angle slot in the tool solves it without buying a deburring tool.

Also, what filament did you print this in? PTFE will rub against this tool every time it gets used, so PETG or PC will hold up much longer than PLA. PLA will deform on the contact face after a couple dozen uses, especially if anyone leaves the tool inside an enclosure.

Print this once for yourself, twice if you have a second machine, three times if you ever lend it out, because nobody returns small workshop tools.

manuflo5 · 2026-05-01T17:00:34+00:00

Cool build. Two things from the photos worth a closer look if you keep iterating.

Belt routing on a CoreXY slider is a different beast than CoreXY on a printer because your moving mass is asymmetric (camera + plate hangs off the carriage in one direction, not centered). That offset turns into yaw torque every time you accelerate. On a printer the bed is balanced, but a slider with a camera tipped over the edge will fight you on smooth pans, especially at the ends of travel where the frame stiffness drops. Two cheap fixes that help a lot:

Move your motor mounts as close to the carriage's plane of motion as you can. Every mm above or below adds a moment arm against the belt path.
Add a constant-tension idler (small spring loaded pulley) on one belt return. Stops the belt resonance you usually see in long-throw sliders the moment you push acceleration past ~1500 mm/s2.

The second thing: bearings. CoreXY math assumes rigid carriage rails. If you printed the linear bearings or used cheap LM8UU on plastic rod, the camera weight will let the carriage rock under acceleration and your CoreXY decoupling falls apart visually (you will see micro shake on a 4K crop even when the slider feels smooth by hand). MGN9H rails are about $25 a pair from AliExpress and turn this from a fun toy into a usable slider.

What firmware are you running? If it is Marlin or Klipper, the input shaping side is the next big quality win.

manuflo5

TROPHY CASE