Show HN: Zenode – an AI-powered electronic component search engine(zenode.ai)
zenode.ai
Show HN: Zenode – an AI-powered electronic component search engine
https://zenode.ai/
39 comments
I see the value here. The problem isn't just the search; it's the trust. The biggest hurdle for Zenode won't be the tech, but convincing an engineer that your AI's summary of a footnote is accurate enough to risk a $10,000 board spin. That's a high bar.
I'd argue the core value isn't just a better search or a faster reader. It's about providing a verified, reliable source of truth. This brings up a key tension: you say the AI isn't yet at your co-founder's level of accuracy, but is that precisely the level of confidence required to replace an engineer's manual check? How do you close that gap? You've got the data, but the trust factor is a different threshold?
I.e. maybe you've built the tool to make the problem faster, but the real win would be a tool that makes the problem safer? The killer feature might not be more speed, but rather a confidence score on every AI-generated fact, with a clear path to the source document so an engineer can verify it. It’s not about avoiding the document entirely; it’s about having a better starting point and knowing exactly what to double-check.
I'd argue the core value isn't just a better search or a faster reader. It's about providing a verified, reliable source of truth. This brings up a key tension: you say the AI isn't yet at your co-founder's level of accuracy, but is that precisely the level of confidence required to replace an engineer's manual check? How do you close that gap? You've got the data, but the trust factor is a different threshold?
I.e. maybe you've built the tool to make the problem faster, but the real win would be a tool that makes the problem safer? The killer feature might not be more speed, but rather a confidence score on every AI-generated fact, with a clear path to the source document so an engineer can verify it. It’s not about avoiding the document entirely; it’s about having a better starting point and knowing exactly what to double-check.
Agreed - trust is the key! That's why we've built in sources with links to the exact location in the datasheet and part documents where the AI found an answer. We're working hard to make sure you can trust its answers, but we know most engineers 'trust but verify'. A (transparent) confidence score is a great idea to improve trust in the answer and sources.
To close the gap, we've built our own Q/A datasets and are training custom AIs how to search and read a datasheet (like a new engineer needs to learn early on). We're concentrating on teaching the AI how to identify key information vs noise as it relates to electrical engineering (differences like 'Voltage' in the Absolute Max vs Recommended section) and where information is likely to be found in a datasheet or app note.
To close the gap, we've built our own Q/A datasets and are training custom AIs how to search and read a datasheet (like a new engineer needs to learn early on). We're concentrating on teaching the AI how to identify key information vs noise as it relates to electrical engineering (differences like 'Voltage' in the Absolute Max vs Recommended section) and where information is likely to be found in a datasheet or app note.
Congrats on the launch!
Toyed around a bit with the site and seems really intuitive; though I used a few of your prebuilt suggestions: "A thermal fuse rated for 75°C and 10A, An optocoupler with 1.5kV isolation, and 5V LDO with 1A output" for example and all come back as not available/no parts available. Just FYI not sure if you want the autofilled suggestions to be the ones that don't produce the results.
Well, that's super annoying, we definitely don't want that, thanks for finding and letting us know! We'll have it fixed shortly.
Who we are
We’re electrical engineers who have lived this pain firsthand for a decade; Brandon (OP) has designed a dozen boards, while I have built over 250 through production.
Years ago we spun up a brand-new product line in two weeks to help salvage a $150M deal. That meant 18-hour days spent picking parts, doing CAD, paying $$$$$ for three-day prototypes and praying everything worked (one did blow up when we accidentally shorted 3 kV into a metal screw on the desk, we nearly died cause of a sleepy brainfart :dizzy_face:). Even from that working version, it took an additional four months of redesigns to get through certifications and into production.
When ChatGPT shocked the world in ’22, we realized that the initial superpower of AI in electronics isn’t in generating designs (too risky — one mistake kills the board), but in digesting the endless unstructured documentation that plagues (benefits? :thinking_face:) our industry.
Congrats on the launch! How much of the component selection pain is actually on the supply chain side? Like, engineers pick parts, but then procurement has to source them without really understanding the technical tradeoffs. Have you talked to purchasing teams about this?
We're learning a lot here, since this always happened AFTER we handed over the BOM. It certainly seems like a pain, particularly when a supplier ends up being a critical bottleneck (always late, overcharging, etc). The number one requested feature from these folks is a way to validate an alternate component, something they can use for themselves BEFORE passing it over to us design engineers for approval (heard some pretty spicy takes on doing this, apparently working with EE's isn't always sunshine and rainbows...)
Congrats on your launch! What's your moat? The big AI players are getting better at reading datasheets and providing contextual guidance and info. It seems like it won't be long until ChatGPT and others will be able to do what your tool does. Thoughts?
Yeah, Moat's are tricky in the age of AI. We think there's always room for a standalone interface in this niche sector, given the sheer amount of data that needs to get presented (with no room for hallucinations). But beyond the data curation (which is actually quite hard) and the custom UI, there's also all the tuning data to optimize for the workflows that come next, like Alternates, schematic awareness, etc.
But yes, OpenAI continues to be the 800lb gorilla 'boogeyman' crushing startups with every new update. Our goal is to remain cockroaches until we find solid distribution!
But yes, OpenAI continues to be the 800lb gorilla 'boogeyman' crushing startups with every new update. Our goal is to remain cockroaches until we find solid distribution!
Thanks for the candid response. I'd expect that because you're not one of the AI giants that you'd be able to work with the different suppliers more easily - licensing deals and even embedded tools in their websites. As far as I can tell, your business model doesn't rely on assimilating as much info as possible, so you're better positioned to work within suppliers' IP and sales pipelines.
That's the hope, but thus far we've been heads down building, and haven't had much time to chat with those folks.
Congrats on launch. Two questions from a user perspective. 1) How do you handle spec changes across document revisions? 2) Can users export the analysis with links to the exact tables and figures in the PDFs?
Solid questions, you've clearly been burned before lol.
1) Document revisions can be a bit hard to handle, but we currently look for any dates or document numbers present, and when possible we just show the latest. However, plenty of times there are no dates OR revision numbers, and we'll get different documents from multiple sources. In this case we show all the documents, and have prompted the AI to flag this risk to the user if the question is answered differently between documents. Not the ideal approach, but working with what we're getting...
2) Hmm, not really. You can share the chat, and the specific response, but the only way to export the source would be to screenshot it... Adding it to our list of potential next features!
2) Hmm, not really. You can share the chat, and the specific response, but the only way to export the source would be to screenshot it... Adding it to our list of potential next features!
Appreciate the detailed response. Showing all conflicting docs and flagging inconsistent answers is a practical interim step. Long term, version tracking across sources could be a real differentiator since engineers live and die by revision history. On exports, even a lightweight “copy link to figure/table” feature would go a long way for collaboration.
Coming from the plant/facility management and process engineering side of things , the pain isn’t PCB parts but pumps, valves, blowers, sensors.
I'd be interested in the industrial-world application of being able to ask things like “which pumps are seawater-rated, 10 bar, 40k hr bearing life, share the same footprint, can run on the same VFD, have BSPT or BSPP threads?”
Do you see Zenode extending into that kind of industrial equipment/knowledge realm?
I'd be interested in the industrial-world application of being able to ask things like “which pumps are seawater-rated, 10 bar, 40k hr bearing life, share the same footprint, can run on the same VFD, have BSPT or BSPP threads?”
Do you see Zenode extending into that kind of industrial equipment/knowledge realm?
We've actually heard this from a few industries, there's definitely a lot of different vertical AI opportunities here! Right now we're very focused on just doing electronic components, but maybe someday!
This looks really promising. Datasheets are such a time sink and a common source of mistakes, so solving that pain point is huge. The Deep Dive feature especially stands out since searching across multiple parts at once feels like what engineers actually need. Curious how you’re thinking about pricing and availability since that seems like the missing piece to make this a daily driver.
Thank you! Yeah, missing pricing and availability is a tough one, but I'm really hoping we'll be able to get it from the distributors in return to funneling better traffic towards their sites...
When normalizing specs across manufacturers, how do you handle cases where units aren’t directly comparable (e.g., different test conditions for “low power” claims, or different temperature reference points)? That’s often where human engineers end up second-guessing the catalog data.
We typically take the 'nominal' value in the datasheet's electrical specs table for parametric specs. If the manufacturer is trying to be sneaky by providing the value with perfect test conditions to improve the numbers, we still give the value because there's no other way to give a credible answer.
However, we saved all the test conditions and similar notes. Maybe we could display the conditions with the specs in the table? That way, any outlier manufacturers would jump out right away.
However, we saved all the test conditions and similar notes. Maybe we could display the conditions with the specs in the table? That way, any outlier manufacturers would jump out right away.
This looks like such a game-changer — congrats on the launch! I’m curious, what have you learned from how engineers were handling these challenges before, and how they’re starting to use Zenode now? It feels like those insights could really shape the next steps.
From all of our customer interviews, almost everyone used Digikey and the occasional manufacturer website to find parts.
Because of that, we're seeing many people put the parameters they're looking for directly into the search. After a few searches, it starts to be clear that you can add more information directly into the search, so queries get a little more complex.
We're getting ready to add features to the AI to help you (interactively) navigate the range of available parts to narrow down your choices and understand what's available when you don't know where to look or what parameters to use. You should be able to ask 'I need to convert a parallel RGB display bus into MIPI' and the AI will show you different families of parts that could do that.
Because of that, we're seeing many people put the parameters they're looking for directly into the search. After a few searches, it starts to be clear that you can add more information directly into the search, so queries get a little more complex.
We're getting ready to add features to the AI to help you (interactively) navigate the range of available parts to narrow down your choices and understand what's available when you don't know where to look or what parameters to use. You should be able to ask 'I need to convert a parallel RGB display bus into MIPI' and the AI will show you different families of parts that could do that.
How does this differentiate from Digi-Key’s parametric search or Octopart’s aggregation?
Digikey's parametrics were the best in industry prior to ours, but they were still really painful to use because of the strings. But to even get to the parametrics, you first have to know in advance the category and subcategory names of the part you're looking for. There are over 3000 different categories, so even once you're pretty good, you're always asking yourself "is what i'm looking for in the logic category or the amplifier category". With Zenode, all this is handled using natural language! And we did a huge overhaul on the parametrics, we turned the strings into structured values and built modern filters that are just waaay easier to use.
Octopart is a bit of a different beast from us. They're crushing the pricing and availability data, which is actually hard to get (a LOT of business development to get access to the realtime values from the distributors themselves). Plus they have a really massive catalog, definitely broader than ours, but they don't have the depth (i.e. the detailed spec and documentation data) that we do because we went to the manufacturer websites to obtain that.
Octopart is a bit of a different beast from us. They're crushing the pricing and availability data, which is actually hard to get (a LOT of business development to get access to the realtime values from the distributors themselves). Plus they have a really massive catalog, definitely broader than ours, but they don't have the depth (i.e. the detailed spec and documentation data) that we do because we went to the manufacturer websites to obtain that.
Congrats on the launch! Deep Dive seems awesome. Curious how you deal with the fact that different vendors describe the same spec in different ways? That’s always been my biggest headache when trying to compare parts.
Yeah, this was actually a nightmare . We had to create "canonicals", which ended up being a mix of regex, machine learning and good old manual human labor. Fortunately, we could usually rely on the datasheet as the 'source of truth', but even that approach failed when faced with the overwhelming amount of part data we were shifting through.
This seems to be incredibly helpful, even if it's not 100% accurate. Then again, isn't that what chatGPT is for? How should I think about Zenode vs. a more generic AI search engine?
The difference between Zenode and something like Perplexity is that Zenode is built specifically for electronics, so it's trained specifically for searching electronics, and the data is much more detailed for electronics. Perplexity has to be good at everything; Zenode is specialized, so it just needs to be the best at working with electronics!
I'm toying around with a custom board and searching here is far better than shuffling through the old Digikey filters. Awesome!
Awesome, that was one of the top reasons we built it, tired of clicking on strings lol. What are you working on specifically? Would love to chat and see what we can do to help!
I'm playing with modifying a BitAxe design (https://github.com/bitaxeorg) by allowing for more chips on a single board. It's theoretically pretty simple, but locating a buck that can handle the current I want to support is tricky!
You should be able to drop the specs you need into the zenode search and get some options; there's always so many choices for buck converters!
Are you looking at a multiphase design? I think I've seen a few chips that control multiphase converters: You'd get a much higher current design with just one IC (and a few extra inductors).
Are you looking at a multiphase design? I think I've seen a few chips that control multiphase converters: You'd get a much higher current design with just one IC (and a few extra inductors).
This is awesome!
Way better than DigiKey’s search.
Thanks, that's why we built it! We're big digikey fans, and have spent hundreds of hours on the site, but that doesn't mean it's without room for improvement!
I was playing around with the search and AI tools and it's pretty sick overall!
The speed of the search was ok, but the AI stuff seems to take a minute. I'm not sure if I've just been spoiled by ChatGPT or if this is a product issue, but it definitely could be faster.
I like the sources that are shown in the deep dive search and the datasheet chat, but deep dive doesn't seem to be taking me to the actual page it references. I know I can just type in the page it says in the pdf viewer, but the datasheet page takes me straight there so why doesn't the deep dive source?
The speed of the search was ok, but the AI stuff seems to take a minute. I'm not sure if I've just been spoiled by ChatGPT or if this is a product issue, but it definitely could be faster.
I like the sources that are shown in the deep dive search and the datasheet chat, but deep dive doesn't seem to be taking me to the actual page it references. I know I can just type in the page it says in the pdf viewer, but the datasheet page takes me straight there so why doesn't the deep dive source?
ChatGPT has definitely spoiled us all, but we're working to improve performance every day. Thus far we've usually prioritized accuracy over speed since there isn't much room for error in this industry, but hopefully our current response time is still faster than reading everything yourself. The sources not linking is a bug, should have it fixed today/tomorrow!
*The problem*
Modern circuit board design is filled with absurdly tedious tasks, where one small mistake can brick a project and cost thousands. The worst (in our opinion) is reading datasheets, which eats up to 25% of the first part of any project: 1. First, you slog through catalogs to find viable parts, using search tools that are still stuck in the dark ages. There are ~80M unique components in today’s supply chain, yet the tools we have to look through them are just digitized versions of the same paper catalogs our grandparents got in the mail.
2. During the design, you spend a ton of time flipping between different 10-100-page PDFs for every component in every subcircuit, hoping like hell you don’t miss some tiny spec in a footnote somewhere that kills your design.
3. And god help you when the requirements inevitably change and now you have to figure out what subsystems are affected!
*What we built*
Zenode is an AI-powered electronics search engine that actually helps engineers find and understand components. Our core features: 1. Largest and Deepest Part Catalog → We have merged dozens of existing part catalogs and documents from major distributors and manufacturers
2. Discovery Search → natural language queries to quickly find categories, set filters, and rank results
3. Modern Parametric Filters → rebuilt from scratch to move off the string values pervasive in industry and build numeric ranges that actually work.
4. Interactive Documents → AI constrained to a single part’s datasheet/manuals. Ask a question, get the answer with a highlighted source for quick reference.
5. Deep Dive → search across dozens of parts simultaneously (“what’s the lowest-power accelerometer available?”) instead of slogging one by one.
*What we learned*
1. By far the hardest part of the last 2 years has been wrangling 3 TB of messy, inconsistent data into something usable. We had to teach the AI how to handle hand-drawn figures, normalize different unit variables and names that mean the same thing, and navigate conflicting information present between different datasheet versions of the same part. It’s been a nightmare
2. We originally built custom PDF parsers and AI extractors, which were best in class for ~3 months until generalized AI passed them. So we stopped reinventing wheels and doubled down on data quality instead.
3. The killer feature wasn’t the AI searching a single part, but what we heard repeatedly from users is that they want the AI to read across multiple parts, hence why we’ve launched deep dive!
*Where it’s strong*
- Speed: rips through a 1,000-page microcontroller datasheet in seconds.
- Breadth: 40M+ part sources unified into one catalog, and more than just datasheets, application notes, errata, etc.
- Comparisons: Deep Dive lets you ask across multiple parts, not just one at a time.
*Where it’s not*
- Pricing/availability: currently outdated (for now we expect folks to check existing aggregators like Octopart).
- Accuracy: good enough to match my mediocre skills; not yet at Collin's level, but we're starting tuning and this will improve rapidly!
*Try it*
It’s live today (zenode.ai). Sign up for a free account and If you put “Hacker News” in during signup in the “where did you hear about us” field, we’ll give you 1,000 bonus credits (once we finish building that, so sometime this week ).
*Feedback we’d love*
1. Should Deep Dive results auto-become filters you can refine further?
2. Do you want the ability to mark preferred parts / exclude others?
3. Is “Deep Dive on a BOM” (alt discovery + manufacturability checks on a list of known components from different categories) the killer feature?