u/Electrical-Taro-4058

To monitor my workout force output effect, I made a 4-to-1 wireless EMG signal acquisition module. Each edge node is single-channel and can be stuck directly on the muscle. Going to the gym tomorrow to test the effect. Mainly want to find the feeling of back engagement, and practice bench press.

Adjusted and redesigned a few times during the last 10 months, and finally got a

​Super good-looking hardcore style wireless EMG signal acquisition module. Supports up to four nodes sync sending, microsecond-level sync, 1600Hz sampling rate, core is still TI INA333, battery life 90 minutes, total weight 16 grams excluding straps. Can be stuck directly to body muscles.💪💪💪

Hope I can have some interesting findings.

​

Recently I saw quite a lot of people using GPT-6 / Codex connected with KiCad, letting AI directly draw schematics and design PCBs.

The result is really amazing.

I am also using it to optimize the circuit of my ESP32 based sEMG acquisition device. But after I actually played with it, I found there is still one obvious problem

which real component should I actually use is still very annoying.

For example that day I want AI to

Find a 3.3V LDO, 500mA+, low quiescent current, preferably SOT-23, and something that can actually be bought.

It recommend a part which looks good in specification, but then I still need to go to JLCPCB and search it, check the exact package, stock, price, Datasheet, and even judge again whether this component is really the most suitable one.

And thinking that every component search requires the LLM to fetch web pages, read Datasheets and do the matching by itself, the token cost is quite large, and it takes time.

So recently I made this small project: AAWChips.

You can understand it as a component data layer specially designed for AI PCB Agents.

For example, it works like:

AI Agent → find the correct real component → parameter matching → stock / price → Datasheet → check suitability → put into schematic

I use LLM technology together with some traditional search engine techniques to make the search more accurate. So there should no longer be situations like “recommend a USB Type-C connector” and then get a bunch of strange connector components.

I also added a cache layer, so common searches can directly reuse the results. This makes the speed and quality of the results much more stable.

Currently it is already connected to a lot of real component data sources, and I make sure every component has been easy to buy in the past 6 months. The result is not just some generic “recommended LDO”, but actual MPN, specifications, package, stock, price, Datasheet and purchase links, yes I added LCSC, jlcpcb and DigiKey link for convenience.

Now I mainly want to see if this tool is actually useful in real AI hardware agent scenarios. Is it worthy for designing the ESP32 projects. Especially for the people who are quite new in this domain. Not everyone has an EE master degree.

Feel free to apply for an API Key and connect it to your own AI coding tool or AI Agent.

Project:

https://aawchips.aawlab.com/

You can use it in any AI agent who supports cli tool use.

# CLI

npm install -g aawchips

export AAWCHIPS_API_KEY=...

aawchips search "10k 1% 0603 resistor"

The reason I didn't make the API completely open is that there are actually quite a lot of LLM-based search, parameter normalization and matching logic behind it, so the running cost is not low. The API Key is mainly to prevent unlimited loops from Agents abusing it.

Anyway, feel free to apply for an API Key and connect it to your own AI coding tool or AI Agent.

https://aawchips.aawlab.com/

After saying so much, the most important thing is that I just want to know whether this tool is actually useful for everyone.

Hope my weeks long work can help people like me, those who are still new in PCB design and ESP32 projects.

This is a long long story.

As a foreigner living in Singapore, it is such a weird place where everyday is summer. The whole island you can drive around in less than two hours, and somehow life still feel boring as hell.

Last Friday after work, I walked out of my office building like an NPC. At the gate I met a friend who does family office trading in the same building, so we went to a small bar, ordered a big beer and fries.

Then usual salaryman talks: boss is toxic, economy is bad, layoffs is coming, AI eating jobs again.

Then somehow we talked about early retirement.

We did some very rough math. Need around 1M USD in financial assets at least. If can make stable 10% annual return, maybe can semi-retire. Worst case go supermarket to be a cashier, at least no boss shouting at me. My current toxic work already giving me almost PTSD.

Then we talked about how much money you would make if you bought semiconductor stocks at the start of the year, and also the recent MRNA cancer vaccine phase 3 success news, which used AI-assisted design.

So I asked him, does your firm use AI for trading?

My friend looked very unimpressed

He said Trading is a lot about taste. LLMs are good at summarizing, but they don't have real trading taste.

I was not convinced.

Mainly because I saw that popular “daily LLM stock trading competition” thing before. It is kind of hype, but still interesting. But that experiment only gave models OHLCV. I feel this is a bit wrong. LLM's strength is not staring at few k lines. It should read news, earnings, filings, biz desc, earning callls, these kind of things.

Only giving OHLCV is not testing LLM. It is testing a bad quant model.

Also honestly, AI already killed so many jobs, helping me retire earlier should not be too much to ask.

So during the weekend I started. I wanted to test two questions:

1. If I give LLM the information visible at that time, can it beat the market?

2. If it looks like it can pick stocks, is it reading the information, or just using company stories it already knows?

Round 1: the result was too good, so I doubted

I used early Jan 2023 as the decision date. I gave several LLMs point-in-time snapshots for a group of large cap stocks, and asked them to pick 10 stocks they wanted to hold for the next 90 days.

However the most tricky part is the point-in-time snapshot. The one that is really annoying to build.

I used yfinance, then tried polygon.io, then opened an FMP account, then did data cleaning until I hated my life, even with codex. Later I found news and historical context data is expensive as hell. Finally I used the snapshot API from plusefin.com.

OK, then I got something from plusefin.com. They are more like a formatted analyst packet. Company info, market data visible at that time, news, fundamentals summary. The useful thing is it can organize what was visible for a stock at a given date.

Then I charged 10 dollars into openrouter.com and tested several models. I did not test claude because too expensive.

Models roughly are:

gpt-5.5 deepseek-v4-flash-0731 glm-5.3-flash kimi-k2.7 and qwen3.8-max

The first round result was very exciting.

Several model portfolios strongly beat SPY. The best model was around +42.7% in 90 days, while SPY was around +7.5% in the same period.

Honestly when I saw this chart, I almost wanted to post directly:

> I asked LLMs to pick stocks. They crushed SPY.

But I feel there must be something felt wrong.

Because these are all famous large cap stocks. After Jan 2023, everyone knows what happened with names like NVDA, META, TSLA. Current models very likely already saw this in training data. So maybe it is not doing research. Maybe it is just remembering the future.

This is very annoying.

Because if this is memory effect, then I can not get any alpha in fact .

So I kept digging.

Round 2: random stock pool, still looks like something

Next I changed to a randomly sampled sp 500 pool, 20 stocks. Same early Jan 2023 start, same 90-day holding period.

This time I did not use SPY as benchmark. Because if the random pool itself is more growth-ish, comparing with SPY is not so meaningful. So I used equal-weight portfolio of the same 20stock pool as benchmark.

Result was still pretty good.

The 20-stock equal-weight pool returned around +6.4%. The 10-stock portfolios selected by LLMs were around +10.6% to +12.7%.

At this point I was a bit excited, but also afraid to believe it.

If round 1 was only memory of big names like NVDA/META, then the random S&P 500 pool should be much weaker. But it still beat the same-pool benchmark.

But then another question came out:

Are ticker and company name itself the biggest cheat code?

For example, when the model sees First Solar, Boeing, NXP Semiconductors, it is not just seeing a string. It sees a whole company narrative, industry position, historical news, and market feeling around that name.

Even more annoying, these names may also make the LLM remember their later performance.

So if I want to know whether it has some more pure trading taste, I need to remove the names.

Of course, this cannot fully remove model memory. But at least remove the most obvious clue: company identity.

Round 3: hide the stock names

So I made a blinding version.

Same 20 stocks. Same early Jan 2023 decision date. Same 90-day holding period.

Difference is, all ticker and company names were replaced with anonymous IDs like Stock_01, Stock_02.

The model can still see the data and text structure inside the snapshot, but it cannot directly know if this is Boeing, First Solar, or Wells Fargo.

Then the result reversed.

In the visible ticker/company version, models had clear positive alpha on average.

In the blinded version, the strong alpha basically disappeared. Some models slightly beat, some underperformed.

It looked more like LLM can call a lot of semantic priors when it sees company identity.

Toxic result.

At least under anonymous condition, it did not show stable stock picking ability.

But this was actually the most interesting part of the experiment. It is not simply saying LLM is useless. It changed the question into:

Where does the LLM's investment judgement come from? From information visible at that time, from company identity and semantic context, or from market memory in training data?

When the model sees FSLR, it may think about solar, policy, growth stock, past performance, market narrative.

When it only sees Stock_04, those things are gone.

Suddenly it feels less magic. Also less fun.

weekly rebalance was more embarrassing

Finally I made a shorter-period version.

Still blinded snapshot, but changed to weekly rebalance. Every week I gave model the anonymous stock snapshots from that time, asked it to pick 10 stocks, and hold until next week.

Result was more direct

all models underperformed the same 20-stock weekly equal-weight benchmark.

Benchmark was around +9.1%. Models were around +4.5% to +5.6%.

From visible, to blinded, to blinded weekly rebalance, average alpha went down all the way.

Hard to cope.

So the ending is I lost.

My friend is probably right: LLM does not look like it can directly trade stocks, at least not in the way I imagined, like feed it some materials and it helps me retire early.

But I also don't think this experiment only says LLM is useless. At least it gave me a new thought:

> If you don't control ticker/company identity, you may not know whether LLM is doing research, or just calling company narrative and historical memory.

In real world, investing is not pure numbers. Company name, industry position, management history, market narrative, these are all part of information. Human trader's taste also cannot be totally separated from these contexts.

At least should be able to do these:

1. Fix the date, try to make sure model only sees information visible at that time.

2. Separate ticker identity and test it alone.

3. Compare performance under visible and blinded conditions.

4. Save every snapshot, prompt, and portfolio the model saw.

I don't think the answer is LLMs are useless for finance. but we need better experiment design to know what exactly it is using to make decisions.

Next step I want to do a real forward-looking version. From now on, every week generate snapshot at fixed time, let model pick stocks, then record future performance in real time.

Then at least no more 2023 historical memory problem.

If people are interested, I can also organize the prompt, pool construction, and blinding method on github.

Finally, please give me a good reason why my experiment is actually wrong.

At least let me feel I still have hope to retire early.

​

I wanted to monitor my muscle activation during workouts, so I planned to build an EMG circuit using an INA333 and an operational amplifier, then send the data to my PC via an ESP32 to analyze the waveforms. I bought one cheap purple INA333 modules from AliExpress, only to discover several fatal issues

They used a simple resistor divider for the Vref without a buffer. This interferes with the INA333's internal resistor network, essentially destroying the CMRR.

The module came with a 100kΩ potentiometer. The gain formula for the INA333 is 1+100k/Rg . Since the INA333 is ideally used for gains between 10x and 50x, about 80% of that range is completely useless

 Between the poor layout and the components acting like antennas for radio interference, the board was basically incapable of capturing a clean signal.

I eventually desoldered the two 103 resistors and added a voltage follower to the Vref. The performance improved significantly after that.

Although my primary focus is sEMG. I know the INA333 has many other applications, so I want to design this as a versatile, general-purpose module.

Let me know if you have any similar applications!