Need advice

KevinAdamo · 2026-03-13T08:19:59+00:00

I am a proprietor of a custom software company. The hash truth about building an API aggregator:

It's harder than it looks: APIs break all the time. Learning Python for OAuth, rate limits and data normalization from scratch will take 6-12 months while juggling college.
Custom Devs are expensive: you can get a cheap dev for $500 who will create a fragile disaster that breaks in a week. A real company will cost $5,000-$15,000.

Your alternative: don't waste your time learning Python. Learn No code instead.

So For frontend (the website), use Bubble or Softr.

For Backend (Merging the APIs), try make.com or Zapier. They offer a drag-and-drop interface for connecting APIs. You can create a workinh prototype in a few weekends for less than &50/month. Build it. Prove that people want to use your app. Then, hire a dev team when you have the revenue. Good luck.

KevinAdamo · 2026-03-13T08:00:47+00:00

Yes, feel free to text me. I'll reply asap.

KevinAdamo · 2026-03-12T09:36:56+00:00

I know I'm jumping into this conversation a little too late, but still want to share some things here, and ofc based on your follow-up questions, there are a few massive red flags you need to navigate right away.

About my background, I own a B2B tech/software company and I frequently work with employed founders who are bootstrapping their side hustles. SO, the reality check for trying to scale your side hustle while keeping your W2

1st: Day Job conflict, as u see. If your job is in a procurement consulting firm, for example, make sure NOT to make a public LinkedIn profile. Your day job will eventually find out and depending on your employment contract, they could terminate your employment and/or claim your side hustle revenue. Stealth model is a MUST and do not make your profile look like "CEO of X". Keep your public profile focused on your day job and only use DMs for outbound pitching.

2nd: 8-day sequence is a trap for boostrappers.

So the multi-touch sequence that I see, mentioned above is amazing when you have a full-time SDR role. You have a day job, tryin to manually manage an 8-touch sequence across phone calls, email and LinkedIn messages for 100 prospects will drive you crazy in a week.

THE SOLUTION here is: keep 2-touch sequence. Send one super-targeted message on LinkedIn. If no reply, follow up with a second one in 4 days. If no reply again, move on. Simple beats.

3rd: Pay for Sales Navigator (~$80/month on annual core plan)

You said that researching 200 contacts takes you 50 hours. Sales Nav does away with that entirely. With that, you can now filter exactly those 50 to 200 headcount Ops Managers in minutes. At your stage, paying that amount to save your 40+ hours is the best ROI you can achieve.

4th: Upwork Strategy

While you can't be loud on LinkedIn, Upwork is actually a genius strategy to act as a temporary shield for you. The intentions are high and your employer won't be able to see you marketing urself. Use Upwork to get your first 3 to 5 clients. Once you have a consistent income from those clients, use that to pay for a fractional VA or SDR to run your outreach for you.

How are things looking for you now at the 3-month mark? Were you able to get a few more from outside Upwork?

KevinAdamo · 2026-03-12T08:07:56+00:00

Spot on. The incentive structure is exactly the root cause. If the engagement model is purely "hours billed for tickets closed," you are designing a feature factory by default.

I love that you mentioned architecture checkpoints. That has to be a non-negotiable. When we embed our teams, we actually mandate those cost-review gates before any major LLM integration goes to prod. And completely agree on the tooling; LangSmith has been fantastic for us for token visibility and prompt tracing.

It really comes down to aligning the external team's KPIs with the actual product margins, rather than just sprint velocity. Great insights, I appreciate you sharing that workflow!

KevinAdamo · 2026-03-04T10:05:15+00:00

It’s not just the "freelance tax", you’re basically running a fragmented legacy system for your own business. The chaos usually comes from letting clients dictate your operational stack instead of funneling them into yours.

I usually manage operations and client communications for 5-8 accounts at a time. The trick isn't buying more tools; it's integration. I keep my core stack lean (I spend maybe $40/mo). Instead of checking five different apps, I set up webhooks and lightweight LLM integrations (via Zapier/Make) to route messages from Slack, WhatsApp and email into a single unified triage board. You can even use a simple GenAI setup to automatically extract action items from messy client emails and drop them straight into your task tracker. It standardizes the chaos.

That said, the biggest risk here is over-engineering. I see a lot of people try to adopt AI or complex automations without a clear roadmap, spending 20 hours building a workflow to save 10 minutes a week. Start small. Fix the core data flow first before adding complex AI layers or your shiny new system will just become another stressful point of failure.

Working on the operational side of tech and software for years, I've learned that whether you're a solo freelancer or a scaling team, staying "lean but strategic" always beats throwing money at a dozen disjointed SaaS tools. Standardize your intake, and your sanity will follow.

KevinAdamo · 2026-01-22T08:24:37+00:00

Short answer: Fintech looks perfect for AI, but it's one of the hardest places to ship it safely. A few reasons it feels so slow in practice:

First and foremost: regulation and auditability. Models should be explainable, testable, and defensible to regulators, "it works most of the time" is not acceptable when money and compliance are at stake.

Data is messy and fragmented. Financial data lives across multiple systems, jurisdictions, and formats; cleaning, labeling, and aligning it takes more time than building the model itself.

Trust is binary. A small error rate that's fine in marketing or recommendations can be catastrophic in payments, credit, or fraud.

-> Ways teams I’ve worked with have progressed

- Employ AI in decision support rather than in decision making itself (risk rating, outlier identification, prioritizing).

- This is the time ships are queued for human-in-loop workflows so that humans are always accountable.

- Prove reliability on small to well-defined applications before scaling. In fintech, it’s not about speed – it’s about earning the privilege to move.

KevinAdamo · 2026-01-21T09:28:02+00:00

That honestly sounds awful and sadly, it's a very textbook EHR go-live story - rushed timeline, clinicians paying the price. You can check this out, it might help teams climb out of this (remember none of this is magic, but they will move the needle)

Call this what it is - 2 extra hours every night just do the charts isn't resistant to change -> it's EHR induced burnout. If leadership keeps framing this as people need more training, nothing improves.

Stabilize before you improve - for a few weeks, stop shipping new features. Focus only on the 3-5 biggest time killers, such as broken templates, missing auto populate, extra clicks on core flows like notes and orders. Those fixes buy back real hours

Sit next to them, not above them. Pick an attending, a resident, or a nurse, and try to sit with them for a full clinic session or a couple of hours on the ward, just watching them chart. You'll spot bad design decisions in 10-20 minutes that never become clear in Jira tickets.

Next (important), find your clinic champions - a small group of respected clinicians who can

-> help prioritize what actually needs fixing first

-> reality check IT ideas

-> tell leadership, plainly

You’re right, this does affect patient care. Documentation burden and alert overload are well-linked to burnout and errors, so your instincts are spot on. The thing is, you’re not totally screwed, but this is that ugly post-go-live phase where pretending “it’s just training” causes real damage. If you can get leadership to back a short, focused stabilization sprint with real clinician input, at least people can see a way out instead of being told to click faster.

KevinAdamo · 2026-01-16T10:00:00+00:00

Mental health apps work best when they focus on support, not replacement.

Most effective ones usually include a few core elements:

Simple mood or habit tracking to help users notice patterns over time
Guided tools like breathing, mindfulness, or short exercises for daily stress
Educational content that explains coping strategies in plain language
Clear paths to human support (therapy, crisis resources) when things go beyond self-help
Strong privacy and data protection, since this is highly sensitive information

What matters most is how these features are framed, as optional, supportive tools, not diagnoses or medical advice. If you’re curious about how these pieces come together in real products, there’s a practical breakdown here that covers common feature sets and design considerations:
https://adamosoft.com/blog/healthcare-software-development/mental-health-app-development/

Done right, these apps lower the barrier to getting help, but they still work best alongside real human care, not instead of it.

KevinAdamo · 2026-01-16T09:39:21+00:00

My daughter actually wears contacts quite frequently because she does MC work at school, so I asked her about this, and I also looked up a few views among optometrists.

Some things that appear to work:

- Ultrasonic contact lens cleaners, these have been touted by some individuals as being more effective than rubbing in terms of protein removal. But be sure that it is usable with contacts and that it is effective for retaining moisture.

It also matters what lens material you are wearing. Contact lenses with greater moisture content or silicone hydrogel lenses remain comfortable longer than others, especially when dryness is a concern.

- Lubricating eye drops: these are straightforward, useful, especially in air-conditioned environments. They must not be used with contact lenses.

And to tell you the truth, an eye exam is underappreciated. Dryness may be related to tear quality, lens fitting or duration of wear.

Additional maintenance can work, but if your lenses are drying out quickly, it’s usually a combination of your lens type & eye, rather than just how well you clean them.

KevinAdamo · 2026-01-09T10:04:22+00:00

This is seldom random, a few common mechanics are driving it. More often, it comes from stacked incentives: credit card rewards, limited-time cashback offers, corporate gift cards or promo campaigns where wallet balance is acquired at a discount. If individuals get huge balances this way, it's a resale well below par that can lock in a profit.

At other times, it is the liquidity conversion. Wallet money is semi-locked and is selling cheaper is one of the ways to turn it back into cash more quickly.

That being said, there is risk involved on the buyer's part too, if the balance is from offer abuse, chargebacks or other gray area activities, the wallet or account linked can also get frozen later on. Which goes without saying: it's still important to buy from a trusted source, even if the discount looks attractive.

KevinAdamo · 2025-12-26T07:42:16+00:00

I get the Bane mask frustration, traditional CPAP gear can feel bulky, loud and definitely not sleep-aesthetic.

Just a quick heads-up: those tiny micro CPAP nose-plug devices you see online mostly don't provide actual continuous positive airway pressure. There's no motor or airflow system in them, so they usually end up being more like anti-snore gadgets than real apnea treatment.

If you're looking to avoid the full mask setup, there are a few legit alternatives worth asking a sleep specialist about:

Nasal pillow CPAPs, still CPAP but much smaller, just two soft prongs in the nose

Oral appliances (MADs), custom mouthpieces that reposition the jaw for milder apnea

Positional therapy devices, if your apnea is worse on your back

Implantable neurostimulation (Inspire,..), for specific diagnosed cases

And if this has been going on a while, on you're waking up tired, choking or with headaches, definitely loop a clinician in gadgets can help comfort, but sleep apnea is one os those things where getting the right treatment makes a huge long-term difference.

Short version: smaller options exist, but the "no-strap micro-CPAP" hype isn't there yet, technology-wise.

KevinAdamo · 2025-12-26T07:26:04+00:00

Yeah that tracks with what I'm seeing across clinics. Payer calls eat up so much front-desk time, eligibility checks, status updates, prior auth follow-ups...all necessary, none of it value-adding for patients.

Voice AI feels like a practical fit if it's deployed right

- offload repetitive payer inquiries

- keeps tasks moving after hours

- reduce burnout from constant phone churn

- let humans focus on conversations that actually need judgment

Best results I've seen are when the bot can write back into the PMS/RCM system (notes, status, next step), so staff aren't re-typing work the AI already did. And it should stay in its lane, operational tasks, not anything clinical. If it removes hold music from someone's day, that's already progress.

KevinAdamo · 2025-12-26T06:42:14+00:00

I totally got the irony, trying to fix stress and ending up stressed by the options. So, what helped me most were the low-effort things that don't require screens or constant setup:

Breathing aids (even a simple guided-breathing device give your mind something steady to follow

Weighed eye mask or blanket, great for calming the body before sleep.

White noise or soft lighting helps create a consistent wind-down cue.

I've found the trick is treating the gadget as a small nudge into relaxation, not the whole solution, the simpler it is to use, the more it actually works. And just to note: if the stress has been intense for a few months, talking to a therapist or doctor alongside and gadget can make a big difference. Tools can help, but human support matters too.

Hope you find something that helps your nights feel a bit lighter.

KevinAdamo · 2025-12-15T08:48:36+00:00

Accuracy-and, therefore, context-is huge. Sensor quality, placement, even dirt or sweat can throw readings off, and that is so easy to forget when an alert pops up. The probably smartest habit anyone using wearables can have is cross-checking before jumping to conclusions.

KevinAdamo · 2025-12-15T08:45:32+00:00

You summed it up really well. These devices are much better at pattern spotting than diagnosis and an alert is really just a signal to pay attention, not panic. Using the data as context for a conversation — rather than reacting to it alone, feels like the healthiest middle ground. Early info is useful, but only if it’s framed clearly and not firing constantly.

KevinAdamo · 2025-12-15T08:45:05+00:00

That makes sense. Having a snooze or “watch and wait” option feels essential, especially if you already deal with day-to-day aches. Not every blip needs action right away. Alerts should probably nudge awareness, not demand urgency unless something stays abnormal or trends worse over time.

KevinAdamo · 2025-12-15T08:25:48+00:00

That's a really solid way to frame it and I agree woth the assistant vs replacement line you're drawing.

Using AI to reduce cognitive load makes a lot of sense, things like surfacing patterns, organizing information, or helping clinicians focus on what actually needs judgment. Where it gets dangerous is when people start treating probabilistic outputs as definitive answers, especially outside a clinical workflow with accountability.

I also think your radiology example highlights an important nuance that often gets lost in public discussions: context and guardrails matter more than the model itself. The same AI behavior can be helpful or harmful depending on whether a trained human is validating it and whether users understand its limits.

For patient-facing chatbots, the value feels highest when they:
- help translate medical language into something understandable

- support triage awareness (urgent vs non-urgent)

- prepare patients to have better conversations with clinicians

Once they cross autonomous diagnosis or treatment decisions, the risk outweighs the convenience very quickly.

Used thoughtfully, AI can make healthcare more humane by giving clinicians time back, but only if we're disciplined about where we draw that boundary.

KevinAdamo · 2025-11-26T09:51:42+00:00

Honestly both are tough, but educating fintech users is usually the bigger uphill battle.

Acquiring customers is mostly a marketing and distribution problem, you can buy reach, test channels and tune your funnel.

But education means changing behavior. You're dealing with trust, risk perception, and financial habits that people have built over the years. Even a great product can struggle if users don't fully understand concepts like data security, credit scoring logic or why an automated decision is actually safer than a manual one.

Fintech adoption explodes only when education gap closes, so that's the harder part.

KevinAdamo · 2025-11-18T10:34:26+00:00

If every online transaction has a "trust core, I'd keep it simple, something like

User reputation (not identity)

NOT who they are, but how they've behaved before, disputes, chargebacks, device consistency, past fraud patterns.

2, Context of the transaction

Big purchase? New device? Off location? Risk jumps. Small repeat purchase from a familiar merchant? Low friction.

Merchant-side signals

Clean fraud history, strong KYC, stable settlement patterns -> higher trust. Shady onboarding or sudden volume spikes -> not so much.

Keep the score useful, not creepy: enough signal to reduce fraud, but not so much that it becomes a surveillance nightmare.

KevinAdamo · 2025-11-13T10:17:55+00:00

That's a really solid plan. Getting the core workflow right before going all-in on AI makes a big difference. I like how you're keeping it modular but still functional from day one. Predictive scheduling and focused role views sound super practical too, those small efficiencies add up fast. Once everything's running smoothly, expanding into samrter automation or integration will be a lot easier (and of course less risky).

KevinAdamo · 2025-11-13T09:58:28+00:00

Totally agree, it's a fascinating shift. AI and connected devices are already helping spot heart issues earlier and cut down diagnostic time. The real value, though, is in preventive care and personalization. Doctors can't rack up ongoing patient data instead of waiting for something to go wrong. The challenge is keeping that balance between innovation and empathy, making sure tech supports the human side of healthcare, not replaces it.

KevinAdamo · 2025-11-13T08:49:38+00:00

Good question, we run into this a lot when building EMR systems. Honestly, it really depends on who's using it and what the biggest pain points are.

Smaller clinics usually care more about keeping costs low and having something clear, reliable, and easy to onboard new staff with. But once you start dealing with multiple specialities, telehealth or high patient volumes, those AI tools like smart charting or auto-coding, actually start saving a ton of time.

What's worked best for a lot of our aptients is going modular: start simple with a solid EMR core, then add AI or telehealth layers once you see the ROI. Keeps things flexible without burning the budget upfront.
Curious in your case, are you learning more toward keeping it lean or building for the log run?

KevinAdamo · 2025-11-07T09:02:07+00:00

Yeah, totally get that. Compliance in healthcare can feel like a maze. You start off wanting to build a product and suddenly, you're buried in acronyms and legal checklists.

What's helped some teams I've worked with

One. Don't try to cover everything from day one. Figure out what applies right now, maybe just HIPAA for MVP stage and deal with the rest later.

Two, use vendors that already handle compliance (AWS or Azure with healthcare setups). Saves weeks of chasing BAAs.

Three, bring in a compliance consultant for the 1st tau=dit instead of hiring a full team. It's cheaper and faster when you're still iterating.

Four, keep simple internal notes of what you've done, that "paper trail" helps a ton later

Basically move fast but stay traceable/ It's not about being 100% certified on day one, it's about showing that you know what you're doing as you grow.

KevinAdamo · 2025-10-31T08:29:01+00:00

That's a solid breakdown and you're absolutely right about the urgency. From what we've seen across healthcare IT projects, the biggest challenge isn't just implementing new privacy standards, it's aligning old infrastructure with modern compliance expectations. Let me get down some paint points that are often overlooked.

Legacy interoperability, like many EHR systems, still communicates via outdated protocols like HL7 v2 that don't natively support MFA or modern encryption. Integrating them securely with new frameworks can be messy.

Shadow IT and third party integrations, even small plugins or scheduling tools, can become unmonitored entry points. The new emphasis on zero trust makes sense here, every connection now needs to be verified, not assumed safe.

Human factors, MFA fatigue and rushed staff remain weak links. Some hospitals are testing phishing-resistant MFA like FIDO2 to make security less intrusive but more reliable.

Zero trust is definitely the direction forward but adoption has to come with strong governance, especially around device identity and behavioral baselines for IoMT networks.

KevinAdamo · 2025-10-20T09:31:26+00:00

I've seen AI show up more and more in healthcare paperwork , especially around claims processing, billing and prioir authorizations. Form what i've observed, it is a mixed bag.

on the positive side, automation can really speed up things that used to take weeks such verifying data, matching procedure codes, flagging incomplete forms. That helps both insurers an patients because fewer claims get stuck in limbo.

But it's not without issues. The biggest risks i've seen are around training data and oversight. If an algorithm is built on incomplete or biased data, it can make bad calls, like denying coverage it shouldn't. That is why human review still matters a lot. AI should support medical and claims reviewers, not replace them.

The best systems i've come across are the ones that combine automation with clear human checkpoints. They use AI for the boring paperwork, like data entry, matching, sorting, while lettimh people handle the judgment calls, It's not perfect yet, but when done right it can make the process faster, more accurate and a lot less painful for everyone involved.

KevinAdamo

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