DEV Community: Tien Nguyen

I run a 209-node automation pipeline on n8n. I modeled what it would cost on per-task billing.

Tien Nguyen — Mon, 29 Jun 2026 02:48:50 +0000

The thing that separates n8n, Zapier, and Make isn't features or polish — it's the billing unit. So I wrote 60 lines of Python to find out where the unit actually bites.

I run the entire content pipeline for a small publication on self-hosted n8n: 10 production workflows, around 209 nodes, on a single cloud box. The most-asked question I get about that setup isn't "why n8n" — it's "wouldn't Zapier have been easier?"

Easier, yes. But the reason I didn't reach for a hosted per-task tool has nothing to do with features and everything to do with one number that nobody puts on the comparison page: the billing unit.

So I modeled it. Here's the short version, then the code.

The three tools bill on three different units

n8n bills per execution — one run of a whole workflow counts as 1, no matter how many nodes it has.
Zapier bills per task — every action that runs is 1.
Make bills per operation — every module run is 1 (now metered as credits).

That sounds like a pricing-page footnote. It's actually the whole decision. n8n's own vs-Zapier page states it plainly: a simple two-step workflow and a complex 200-step AI agent both count as one execution — and that same 200-step agent is "up to ~200 tasks" on a per-task tool.

My pipeline is built out of exactly the kind of long workflows that punishes. To make this concrete, here are three real ones:

Workflow	Nodes	n8n cost per run	Per-task cost per run
SF-2 script generator	27	1 execution	~27 tasks
SF-4 render	28	1 execution	~28 tasks
SF-5 publisher	33	1 execution	~33 tasks

SF-2 is a webhook receiving a content ID, a Postgres fetch, a Code node that assembles a prompt, an HTTP call to an LLM, a parser, two guard If nodes, a write-back, and a respond node. Twenty-seven nodes, one execution, done in seconds. Nothing exotic — just a legible chain of small steps. But on a per-task meter, every one of those nodes is a coin in the slot.

The model

No API, no network — just the arithmetic the pricing pages bury. The node→task mapping is 1:1, which is the vendors' own framing (the "200 steps ≈ 200 tasks" line above).

# Entry-tier quotas, verified live 2026-06-29
N8N_STARTER = {"quota": 2_500,  "unit": "executions", "price": "€20/mo annual"}
ZAPIER_PRO  = {"quota":   750,  "unit": "tasks",      "price": "$19.99/mo annual"}
MAKE_CORE   = {"quota": 10_000, "unit": "operations", "price": "$9/mo annual"}
# n8n Community (self-hosted): unlimited executions, $0 software.

def tier_ceiling(nodes, quota):
    """Max runs/month before a per-unit tool's entry quota is exhausted."""
    return quota // nodes

for name, nodes in {"SF-2": 27, "SF-4": 28, "SF-5": 33}.items():
    print(name, nodes, "nodes ->",
          "n8n:", N8N_STARTER["quota"], "runs |",
          "Zapier:", tier_ceiling(nodes, ZAPIER_PRO["quota"]), "runs |",
          "Make:", tier_ceiling(nodes, MAKE_CORE["quota"]), "runs")

Output:

====================================================================
ONE WORKFLOW: where its monthly run budget runs out at the entry tier
====================================================================
workflow                 nodes   n8n runs  Zapier runs   Make runs
SF-2 script generator       27       2500           27         370
SF-4 render                 28       2500           26         357
SF-5 publisher              33       2500           22         303

Read the SF-4 row. It's a 28-node workflow. On n8n that's 1 execution per run — 2,500 runs fit the €20 Starter tier, and it's unlimited if you self-host for free. On Zapier's $19.99 Professional plan, those 28 tasks-per-run mean you exhaust the entire 750-task budget at 26 runs a month. Roughly once a day. One workflow. A single moderately-branchy automation, run daily, eats a whole hosted plan.

Now scale it to the whole pipeline

One piece of content doesn't touch one workflow — it flows through all ten. So the real unit of work is "one content item, end to end": ~10 executions on n8n versus ~209 task-equivalents on a per-task tool.

  items/mo    n8n exec   Zapier tasks
        50         500         10,450
       100       1,000         20,900
       250       2,500         52,250
       500       5,000        104,500

At 100 content items a month, that's 1,000 n8n executions — comfortably inside the 2,500 Starter tier — versus 20,900 Zapier tasks, which is many tiers above the $19.99 plan. Self-hosted, the n8n number costs $0 in software. And the gap doesn't close as you grow; it widens, because every node you add to a workflow is free on an execution meter and another coin on a task meter.

This is the same shape n8n's marketing claims, but it's worth deriving yourself rather than taking on faith: a 30-step workflow run 1,000 times is 1,000 executions (fine) or 30,000 tasks (a plan several times more expensive). The more your automation actually does, the more lopsided it gets.

The honest other side

I'm not going to pretend the execution meter is a free lunch, because the bill isn't where n8n costs you — the operations are:

The learning curve is real. It's the top complaint in Capterra reviews, and fair: n8n expects comfort with APIs, JSON, and the occasional line of JavaScript. The moment you need to reshape data between steps, you're in Code-node territory. Zapier's plain-English Copilot will genuinely get a non-coder to a working automation faster.
Self-hosting is an ops burden you now own. Docker plus Postgres is a ~30-minute stand-up, but you also own the upgrades, the backups, the monitoring, and the occasional 2am debug. A hosted tool absorbs all of that into the price.
A sizing gotcha: sub-workflow calls, manual test runs, and automatic retries each count as executions too, so "2,500 should be plenty" can surprise you. Model your real run frequency, not your happy-path count.
"Free" has a license asterisk. The Community edition is source-available under a fair-code license, not OSI open source — you can't resell it as your own hosted service.

For what it's worth, the reliability has held: pulling our live execution history off the n8n API, the last 93 retained executions came back 100% success. For a stack that renders video and posts to three platforms unattended, that's the bar I cared about. But that's my workload. If you're wiring a form to a CRM and a Slack ping, none of this math matters and you should just use Zapier.

The takeaway

"Which automation tool is cheapest" is the wrong question, because the tools don't even bill in the same unit. The right question is does your work look like a few short automations, or a few long ones run often? Short and occasional → the per-task polish of Zapier is worth paying for. Long or high-volume → the execution meter (and the free self-host) is a structural cost advantage that compounds with every node you add.

If you want the full version of that decision across all six tools in the category — who each one is actually for, and how their very different meters play out — I wrote it up in a roundup of the best AI automation tools. The one-line version: the best tool isn't the cheapest, it's the one billed like you build.

The 60-line model is reproducible — clone the quotas, swap in your own workflow node counts, and find your own crossover before you commit to a meter.

Two opposite designs for AI meeting notes: transcribe everything vs enhance what you typed

Tien Nguyen — Tue, 16 Jun 2026 05:17:06 +0000

I ran the same meeting through two AI notetakers, Otter and Granola, expecting to compare accuracy. The accuracy was close. What actually separated them was something more interesting: they aren't built to do the same job. They sit on opposite models of what a meeting "note" even is, and once you see the two designs, the "which is better" question dissolves into "which model fits your workflow."

Two models

Strip away the branding and you get two functions. This is conceptual — I'm describing the designs, not either company's internal code:

# Model A — transcribe everything, then summarize  (Otter)
notes = summarize(transcribe(audio))

# Model B — enhance what you typed  (Granola)
notes = enhance(user_bullets, transcribe(audio))

Model A captures the whole call verbatim, labels who spoke, and then runs an extractive summary over that complete record. The transcript is the primary artifact; the summary is derived from it. You did nothing during the meeting; the tool recorded everything.

Model B inverts the inputs. You jot a few rough bullets during the call, and the tool merges your notes with what it heard — using your fragments as a seed and filling in the specifics from the audio. The finished summary is the primary artifact; the raw transcript is secondary (Granola even deletes the audio once it has the text).

The difference isn't cosmetic. The two models take different inputs, so they produce structurally different outputs and fail in different ways.

What that looked like on one meeting

I fed both an 80-second, two-speaker product meeting (synthetic voices, so I knew the exact right answer) and, for Granola, typed six sloppy bullets while it listened — fragments like "get API latency under 200ms, file P1."

Otter (Model A) produced the more literal, verbatim transcript and labeled both speakers cleanly. It was a complete, attributed record — though it dropped the quarter off one figure, turning "Q3" into "Q." If the word-for-word record is what you need, this is it.
Granola (Model B) garbled a line in its raw transcript, but that's not what it's for. Its enhanced summary — my six bullets fused with what it heard — was the most complete and usable write-up either tool produced. It pulled in specifics my notes left out (an activation jump, a flagged latency spike, an owner to loop in) and organized them around the things I'd already signaled mattered by typing them.

Same meeting, opposite artifacts: one a faithful transcript, the other a send-ready recap.

Why the input model decides the output

Here's the part worth internalizing if you build with LLMs at all. Model B tends to produce more useful notes not because of a better model, but because it has a better input: your relevance signal. When you type "file P1," you've told the system this mattered — a piece of human judgment an extractive summarizer over a raw transcript simply doesn't have. The augmentation model gets to condition on what a participant already decided was important.

That also predicts its failure mode exactly: type nothing and Model B collapses toward Model A. Granola's whole edge assumes you take notes; give it no bullets and you lose the augmentation that makes it special. Model A has no such dependency — it records everything whether you participate or not, which is precisely why it's the safer default for a meeting you can't also take notes in.

So the trade is structural, not a quality ranking:

Model A (transcribe-everything) gives you a verbatim, speaker-labeled, searchable archive, and keeps the audio for playback. The transcript itself is a deliverable. Best when the record matters — exact quotes, who-said-what, a meeting you need to reconstruct later.
Model B (enhance-your-notes) gives you a finished recap seeded by your own judgment, plus an ask-anything memory across your notes — but it deletes the audio and assumes you're an active participant who jots. Best when the output matters and you're in the room thinking.

(Audio capture — whether a bot joins the call or it records your system audio — is a separate axis from note generation; I'm only talking about the latter here.)

The decision, and the build lesson

For choosing a tool, the framing that actually helps isn't "which is more accurate." Both are fine on clean audio. It's "do I want a record or a recap?" A record you can search and quote → the transcribe-everything model. A finished write-up you'll forward without editing → the enhance-your-notes model. I put the full head-to-head — pricing, privacy postures, and which one wins for which kind of meeting — in this tested comparison of Otter and Granola, but the model question is the one to settle first, because it's upstream of every feature.

And the lesson for anyone building AI summarization: the cheapest way to make a summary feel smarter is often to capture the user's relevance signal, not to upgrade the model. A few human-typed bullets as a conditioning input beat a larger model summarizing cold, because the human already solved the hardest part — deciding what mattered. Design for that input and your "dumb" summarizer punches well above its weight.

If you've built a summarizer that captures relevance signal some other way — reactions, highlights, edits-as-feedback — I'd like to hear how in the comments.

You can't benchmark an AI notetaker against a real meeting — you don't know the right answer. So I generated the meeting.

Tien Nguyen — Mon, 15 Jun 2026 07:19:29 +0000

I wanted to know which AI notetaker transcribes most accurately — Granola, Fathom, or Otter. So I did the obvious thing: I recorded a real meeting, ran it through all three, and compared the transcripts.

That experiment is worthless, and it took me one afternoon to see why. To score a transcript you need the correct transcript to score it against. But the only record of what was actually said in my meeting was… the transcripts I was trying to grade. I was marking the exam with the students' own answers. There was no answer key.

The fix turned out to be the interesting part, and it's a trick worth stealing for any speech-to-text evaluation: if you don't have ground truth, manufacture it. Write the script first, synthesize the audio from it, and now the exact words are something you typed — not something you have to reconstruct after the fact.

Generate the meeting, keep the answer key

I wrote an 80-second, two-speaker product meeting and deliberately stuffed it with the tokens that actually matter in a work call and that ASR engines love to fumble: quarter labels (Q3, Q2), percentages (5.2%, 6.8%, 41%, 58%), dollar figures ($16 → $19), jargon (churn, cohort, activation, SSO, deep links, p95, P1), names (Sarah, David, Priya, Marcus), and a few real action items with deadlines.

Then I gave each speaker a distinct ElevenLabs voice and rendered the turns through the API. The whole harness is a bash script — two voice IDs, one text-to-speech call per line, and ffmpeg to stitch the turns together with a beat of silence between them so the diarizers have a clean boundary to find:

SARAH=21m00Tcm4TlvDq8ikWAM   # Rachel (female)
DAVID=pNInz6obpgDQGcFmaJgB   # Adam (male)
MODEL=eleven_multilingual_v2

gen () { # $1 voice  $2 outfile  $3 text
  curl -sS -X POST "https://api.elevenlabs.io/v1/text-to-speech/$1" \
    -H "xi-api-key: $ELEVENLABS_API_KEY" \
    -H "Content-Type: application/json" \
    -d "$(python3 -c 'import json,sys; print(json.dumps({"text":sys.argv[1],"model_id":sys.argv[2]}))' "$3" "$MODEL")" \
    --output "$WORK/$2"
  # fail loud if the API returned a JSON error instead of audio
  if file "$WORK/$2" | grep -qi 'json\|text'; then echo "ERROR in $2:"; cat "$WORK/$2"; exit 1; fi
}

gen $SARAH t00.mp3 "Morning, David. Before we start, did the Q3 churn numbers come in?"
gen $DAVID t01.mp3 "They did. We closed at five point two percent monthly churn, down from six point eight in Q2..."
# ...eight more turns...

# concat in order with a short silence between turns for cleaner diarization
ffmpeg -y -f lavfi -i anullsrc=r=44100:cl=mono -t 0.4 -q:a 9 "$SIL" >/dev/null 2>&1
for i in 00 01 02 03 04 05 06 07 08 09; do
  echo "file '$WORK/t$i.mp3'" >> "$LIST"; echo "file '$SIL'" >> "$LIST"
done
ffmpeg -y -f concat -safe 0 -i "$LIST" -c copy "$WORK/meeting.mp3"

A couple of small things that matter in practice:

Spell numbers as words in the input ("five point two percent"), or the TTS will read "5.2%" inconsistently. You want the audio to be unambiguous; the notetaker's job is to turn it back into "5.2%", and whether it does is exactly what you're testing.
Fail loud. The ElevenLabs endpoint returns a JSON error body with a 200-ish shape when something's wrong (bad voice ID, quota), and if you blindly write it to meeting.mp3 you'll "transcribe" a corrupt file and not know why. The file check above bails instead of silently producing garbage.
Insert real silence between turns. Diarizers lean on pauses to find speaker boundaries. A 0.4s gap is realistic and gives every tool a fair shot at separating Sarah from David.

Now I had a clip and a text file of exactly what's in it. The answer key is just the script I wrote. Same clip, three tools, one rubric.

The surprise: clean-audio accuracy is a wash

Here's the result I didn't expect. On clean, two-voice audio with no crosstalk, all three are essentially excellent. Otter clocked in around 99% word accuracy. Fathom's transcript was the most accurate of the three. Granola kept the substance and garbled maybe a line. If you rank these tools by raw word error rate on a clean clip, you basically can't tell them apart — they're all near the ceiling.

Which means raw accuracy is the wrong thing to benchmark. It's table stakes. The differences that decide which tool you should actually use live in two places the overall WER number hides: what each one does with the handful of tokens that carry the meeting's meaning, and whether it tells you who said what.

Scoring against the answer key, only on cells I could verify from the actual transcripts:

Tool	Clean-audio accuracy	Quarter labels (Q3 / Q2)	Speaker labels
Fathom	most accurate of the three; summary numbers all correct	kept "Q3 churn", "6.8 in Q2"	tracks owners in the summary (David, Priya)
Granola	near-perfect; garbled a line or two	kept "Q2"; wrote "P95" and "$16 → $19" cleanly	none on an ad-hoc capture — one unlabeled stream
Otter	~99% word accuracy	collapsed "Q3"/"Q2" to "Q"; "tag it P1" → "tag at p1"	correct diarization — the only one that labeled the two speakers

Read that table and the headline accuracy number inverts in importance. Otter, the strongest pure transcriber on paper, is the one that quietly collapsed both "Q3" and "Q2" into a bare "Q" and turned "tag it P1" into "tag at p1" — and in a business meeting the quarter is not a throwaway word, it's the thing the whole churn number is anchored to, just as "P1" is the difference between a ticket that pages someone and one that doesn't. Meanwhile Otter is the only one that reliably told me David said the latency line and Sarah owned the pricing test, because it's the only one that diarizes. Granola, bot-free and great on the numbers, handed me one unlabeled stream of text for an ad-hoc capture — fine for a solo note, useless if you need attribution.

So the "best" tool flatly depends on what your meetings need, and it isn't the WER ranking:

If you need speaker attribution, only one of these reliably gives it to you on a casual capture.
If you need the numbers and jargon dead-on, the leader on overall accuracy is the one that fumbled the quarter labels.
If you take sensitive client calls, the bot-free tools change the calculus entirely (no visible recorder joining the call), and that's a different axis again.

I put the full head-to-head — the per-tool summaries, the privacy and free-tier trade-offs, and which one I'd hand to which kind of user — in our tested comparison of the best AI notetakers, because the short version ("they're all accurate") is true and also completely unhelpful for choosing one.

What the synthetic-ground-truth trick is good for (and not)

The methodology generalizes well beyond notetakers:

Any speech-to-text eval. Captions, voice-command parsers, call-center QA — if you script the audio, you get a free, exact reference transcript and a repeatable test you can re-run after every model update.
Adversarial token design. Because you write the script, you can plant the things that break engines on purpose: homophones, acronyms, numbers, code-switching, overlapping names. Real meetings rarely stress all of those in 80 seconds; a synthetic one can.
Regression tracking. The clip is deterministic input. Re-run it next quarter and you'll see whether a vendor's "improved" model actually improved on your hard tokens or just the easy ones.

The honest caveat, stated plainly: synthetic clean audio is the friendly case. Two distinct TTS voices with no crosstalk, no accents, no room noise, no people talking over each other is the easiest input these tools will ever see — which is exactly why it's good for isolating the meaning-carrying-token and diarization differences, and exactly why you should not read a 99% here as 99% in a real four-person standup. It's a controlled benchmark for comparing tools against each other on identical input, not a prediction of real-world accuracy. For that, you still have to test on your own messy calls — but now you have a clean baseline to measure the messiness against.

The whole harness is ~40 lines of bash. Swap in your own script, plant your own landmines, point it at whatever transcribers you're weighing, and you'll have an answer key nobody can argue with — because you wrote it first. If you've got a better way to get ground truth for an ASR comparison without hand-transcribing hours of audio, I'd genuinely like to hear it in the comments.

Murf bills AI voiceover by the second, ElevenLabs by the character. I did the arithmetic to find where each one wins.

Tien Nguyen — Thu, 11 Jun 2026 03:30:48 +0000

I generate a lot of synthetic speech for side projects, and I keep two text-to-speech subscriptions open: Murf and ElevenLabs. For a long time I treated "which is cheaper" as a fixed fact — one number versus another. It isn't. The two tools meter you on different units, and once you notice that, the cheaper tool stops being a property of the price page and becomes a property of your script.

Here is the whole thing in one sentence: Murf charges you for the duration of the audio, ElevenLabs charges you for the characters in the text. A pause costs money on one and nothing on the other. I modeled it to find exactly where the line falls, and the answer surprised me enough to write it down.

Two meters

Murf meters "voice generation time" — you get a fixed budget of audio hours per month (2 hours on the $29 Creator plan), and every second of generated speech draws it down, regardless of what's in it.
ElevenLabs meters credits, where 1 credit = 1 character of input text on the standard multilingual model (121,000 credits/month on the $22 Creator plan). Silence, pauses, and slow delivery cost nothing extra — they aren't characters.

So the natural unit to reason in is characters per second of audio — how densely you pack text into time. Call it cps. A rapid ad read crams a lot of characters into each second; a paced e-learning module with breathing room spreads few characters over many seconds. Same words, different cps, depending on delivery.

Normalize both meters to a common basis and the asymmetry pops out:

@dataclass(frozen=True)
class DurationPlan:        # Murf — billed per second of audio
    name: str
    price: float
    hours_per_month: float
    def dollars_per_audio_hour(self, cps: float) -> float:
        return self.price / self.hours_per_month   # cps is irrelevant here

@dataclass(frozen=True)
class CharPlan:            # ElevenLabs — billed per character
    name: str
    price: float
    chars_per_month: int
    def dollars_per_char(self) -> float:
        return self.price / self.chars_per_month
    def dollars_per_audio_hour(self, cps: float) -> float:
        return self.dollars_per_char() * cps * 3600   # cps drives the cost

Look at the two dollars_per_audio_hour methods. Murf's ignores cps — it's a flat rate per hour of audio. ElevenLabs' is linear in cps. One is a horizontal line, the other is a ray through the origin. Two lines cross exactly once, and that crossing is the whole story.

Unit costs (the meters, normalized):
  ElevenLabs : $0.182 per 1,000 characters
  Murf       : $14.50 per audio-hour (monthly), $9.50 per audio-hour (annual)

The break-even is a speaking density

Set Murf's flat $/hour equal to ElevenLabs' cps-driven $/hour and solve for cps:

def breakeven_cps(dplan, cplan):
    flat_per_hour = dplan.dollars_per_audio_hour(0)        # density-independent
    return flat_per_hour / (cplan.dollars_per_char() * 3600)

Break-even speaking density (characters per second of audio):
  vs Murf Creator (monthly)  :  22.2 cps
  vs Murf Creator (annual)   :  14.5 cps
  Below the break-even, ElevenLabs is cheaper; above it, Murf is.

Now the numbers mean something. Below the break-even density, ElevenLabs wins; above it, Murf does. So how dense is real speech? Natural English narration runs about 13–16 cps; a brisk, dense read pushes toward 18–20; a paced read with deliberate pauses drops to 10–12. (You don't have to trust me — cps is just characters / audio_seconds, so you can measure it on any clip you've already made.)

Hold that against the two break-evens:

Murf monthly breaks even at 22.2 cps — higher than essentially all real speech. On monthly billing, ElevenLabs is cheaper for basically anything you'd actually narrate.
Murf annual breaks even at 14.5 cps — right in the middle of the real range. Now it's a genuine coin-flip that depends on your delivery.

Sweeping a realistic range of content makes it concrete (cps is the input you'd measure from your own audio, so I sweep it rather than pretend there's one true value):

content type                       cps  EL $/hr  Murf mo  Murf yr  cheapest
---------------------------------------------------------------------------
Slow, paused e-learning            9.5     6.22    14.50     9.50  ElevenLabs
Measured narration / explainer    12.5     8.18    14.50     9.50  ElevenLabs
Audiobook, natural pace           15.0     9.82    14.50     9.50  Murf annual
Conversational / podcast          16.5    10.80    14.50     9.50  Murf annual
Brisk ad read                     19.0    12.44    14.50     9.50  Murf annual
Rapid dense voiceover             22.5    14.73    14.50     9.50  Murf annual

(One honest caveat: Murf's flat $9.50/$14.50 per hour assumes you actually use the full 2-hour budget. It doesn't roll over, so if you generate less, your real per-hour cost is higher than the table — never lower. ElevenLabs unused credits also expire, but the per-character price is the per-character price whether you use 10 or 10,000.)

Pauses are the punchline

Here's the part that made me laugh. Murf's pitch to e-learning teams leans hard on its delivery controls — insert exact pauses, slow the pace, let a sentence breathe. Good instructional design. But a pause is free characters and paid seconds, which means every bit of that polish drags you down the cps axis, toward the region where Murf is the more expensive meter.

Take one minute of narration at 15 cps and add 12 seconds of deliberate pauses — about a one-second beat every five seconds, which is normal for teaching:

Cost of 12s of deliberate pauses on a 60s, 900-char narration:
  before: 60s audio, 900 chars -> 15.0 cps
  after : 72s audio, 900 chars -> 12.5 cps
  Murf (monthly): $0.2417 -> $0.2900  (+20% -- you pay for the silence)
  ElevenLabs    : $0.1636 -> $0.1636  (+0% -- pauses are free characters)

The script didn't change — same 900 characters. The pacing did, and on a duration meter pacing is the bill. The exact feature that makes Murf good for paced instructional content is the feature that makes its meter the wrong one for paced instructional content. That's not a knock on the voices; it's a property of charging by the second.

This is why the head-to-head verdict on Murf is so delivery-dependent rather than a flat "cheaper/pricier," and the meter is only half of it — the other half is whether the studio around the voice (the timeline, the music library, the project system) earns the subscription regardless of the per-hour math. I went through all of that the slow way in a full hands-on Murf AI review, and the short version is that Murf sells the studio, not the per-character price — which lines up exactly with what the arithmetic here says.

Takeaways for anyone comparing metered tools

Check the unit before the price. Two tools quoted in different units (seconds vs characters, requests vs tokens, rows vs gigabytes) can't be compared by their headline numbers. Normalize to a shared basis first; the cheaper one often flips.
A flat meter and a usage meter cross exactly once. Find that crossing and you've turned "it depends" into a single threshold you can check your real workload against.
The binding variable is usually one you don't control on the price page. Here it's your speaking density — a property of your content, not the vendor's. Measure it (characters / audio_seconds) before you commit to a year.
Watch for features that fight the meter. When the thing a tool is good at (pauses, retries, verbose output) is billed by the axis that thing inflates, the value proposition quietly works against you.

The whole model is ~90 lines of stdlib — swap in your own cps and plan numbers and it'll tell you which meter you're really on. And if you've found a worse unit mismatch than billing narration by the second while selling pause controls, I'd love to read it in the comments.

Descript bills you on two meters at once. I modeled it in Python to find which plan actually fits.

Tien Nguyen — Wed, 10 Jun 2026 05:18:40 +0000

Descript is the rare creative tool that earns its hype: you edit video by deleting words in a transcript, and the footage disappears with the text. I have shipped real podcast and talking-head edits through it. But the question that kept tripping me up was not "can it do the edit" — it was "which plan am I supposed to be on," and that turns out to be genuinely hard to answer, because Descript meters you on two independent budgets at once.

I am a builder, so I did what I do with any pricing I do not trust: I modeled it. Here is what fell out, and why the answer is less stable than Descript's pricing table makes it look.

Two meters, not one

In a 2025 overhaul Descript scrapped its old "transcription hours" plans and rebuilt everything on two separate meters you have to track simultaneously:

Media Minutes are spent when you upload or record media — transcribed or not, used in the final cut or not.
AI Credits are spent per AI action: Studio Sound, filler-word removal, Eye Contact, voice generation, and every Underlord co-editor command.

Two traps live in that design before you write a line of code:

Neither meter rolls over. An unused balance resets on your billing date. You are effectively paying for your heaviest month, every month.
Media Minutes are billed on upload. Import three hours of b-roll, use ten seconds, and you still spent three hours of your allowance. Descript says so directly in the Media library: "each file added uses media minutes."

So picking a tier is a two-dimensional bin-packing problem, not a single-number lookup. That is exactly the kind of thing a tiny model clarifies.

The plans, as data

Here are the four consumer tiers as a dataclass — allowances pulled straight off Descript's live pricing page in June 2026. Two columns deserve a flag before you trust them. The Free plan's 100 credits are one-time, not a monthly refill (a landmine I will come back to). And Creator and Business each advertise a "+bonus" on top of the standing allowance — but with no fine print anywhere on the page saying whether that bonus recurs monthly, applies only to annual billing, or is a one-time signup sweetener. So I model the bonus as a separate, optional ceiling rather than baking it into the headline number:

from dataclasses import dataclass

@dataclass(frozen=True)
class Plan:
    name: str
    price_monthly: float   # USD
    ai_credits: int        # STANDING monthly allowance (Free is one-time)
    media_minutes: int     # STANDING monthly allowance
    bonus_credits: int     # advertised "+N bonus" — recurrence UNDOCUMENTED
    bonus_minutes: int     # advertised "+N bonus hours" -> minutes — same caveat
    topups: bool

    def credits(self, count_bonus: bool) -> int:
        return self.ai_credits + (self.bonus_credits if count_bonus else 0)

    def minutes(self, count_bonus: bool) -> int:
        return self.media_minutes + (self.bonus_minutes if count_bonus else 0)

PLANS = [
    Plan("Free",      0,  100,   60,    0,   0, topups=False),
    Plan("Hobbyist", 24,  400,  600,    0,   0, topups=True),
    Plan("Creator",  35,  800, 1800,  500, 300, topups=True),   # +500 credits / +5 hrs "bonus"
    Plan("Business", 65, 1500, 2400, 1000, 600, topups=True),   # +1000 credits / +10 hrs "bonus"
]

The topups flag matters more than it looks: the only tier with no top-up path is Free — there you just wait for the reset. The paid tiers all let you buy more credits or minutes when you run dry, which is its own trap, because the easiest way to "fix" a too-small plan is to keep buying top-ups until you have quietly out-spent the tier above it.

Describing a month of work

Now model the work the way you actually experience it, not the way the marketing page frames it:

@dataclass
class Workflow:
    episodes_per_month: int
    raw_minutes_per_episode: float    # what you RECORD, not what you keep
    rerecord_factor: float            # 1.2 = you re-upload 20% extra takes
    unused_import_minutes: float      # b-roll you add but never finish
    studio_sound_passes_per_episode: int
    other_ai_actions_per_episode: int     # Underlord, filler removal, eye contact...
    est_credits_per_other_action: int     # YOUR estimate — Descript won't tell you

    def media_minutes(self) -> float:
        recorded = (self.episodes_per_month
                    * self.raw_minutes_per_episode
                    * self.rerecord_factor)
        return recorded + self.unused_import_minutes

    def ai_credits(self) -> int:
        studio = self.studio_sound_passes_per_episode * 10   # the one documented cost
        other = self.other_ai_actions_per_episode * self.est_credits_per_other_action
        return self.episodes_per_month * (studio + other)

See that 10 hardcoded in ai_credits? Studio Sound at 10 credits per use is the only per-action credit cost Descript publishes. Every other AI action — and Underlord, the feature they sell hardest, is the hungriest of them — has no documented price. You discover the cost by watching your balance drop. That single fact is why budgeting Descript feels like guessing, and the model makes the guess explicit instead of hiding it.

Finding the right plan is then: cheapest plan whose both meters cover the month. The count_bonus switch lets me ask the question twice — once trusting only the standing allowance, once believing the advertised bonus — because that ambiguity turns out to be where the money is.

def cheapest_fit(wf, count_bonus):
    need_min, need_cr = wf.media_minutes(), wf.ai_credits()
    for plan in sorted(PLANS, key=lambda p: p.price_monthly):
        if plan.minutes(count_bonus) >= need_min and plan.credits(count_bonus) >= need_cr:
            return plan
    return None   # overflows every plan → top-ups or wait for reset

Running it: the answer moves

Take a concrete creator — a weekly podcast, four 60-minute records a month, 20% re-records. First, leaning on AI only lightly (two Studio Sound passes per episode, nothing else). I print the answer twice: once on standing allowances, once assuming the bonus recurs.

Weekly podcast, Studio Sound only:
  needs 288 media minutes + 80 AI credits / month
  -> standing allowances only : Hobbyist ($24/mo)
  -> if the '+bonus' recurs   : Hobbyist ($24/mo)

Comfortable on Hobbyist, bonus or no bonus. Now the same person starts actually using the features Descript advertises — Underlord rough cuts, filler removal, eye contact — eight AI actions an episode, plus a couple hours of imported b-roll they trim away:

Same creator, leaning on Underlord (est. 25 credits/action):
  needs 408 media minutes + 880 AI credits / month
  -> standing allowances only : Business ($65/mo)
  -> if the '+bonus' recurs   : Creator ($35/mo)

Media minutes barely moved (408, still inside Hobbyist's 600). Credits are what blew up — and look at the split. On the standing 800-credit Creator allowance, 880 overflows and you land on Business at $65. If that "+500 bonus" actually recurs, Creator's real ceiling is 1,300 and the same workflow fits for $35. Whether this costs you $35 or $65 a month turns on a bonus whose recurrence Descript never states. That is not an edge case I engineered; it is the gap between the two numbers the pricing page puts side by side.

Now hold the workflow fixed and sweep the other undocumented number — the per-action credit cost — across both allowance assumptions:

Sensitivity to the undocumented per-action cost (heavy workflow):
  est/action   credits/mo   standing-only   if-bonus-recurs
  10              400       Hobbyist        Hobbyist
  25              880       Business        Creator
  40             1360       Business        Business

Two unpublished numbers, and the middle row swings the bill between three tiers depending on how you guess them. This is not a knock on the editor, which is excellent. It is a warning about the meter. When I dug into where the credits actually go and ran my own projects through every tier, the recurring failure mode was always the same: people priced themselves on the headline allowance, then the AI features they were sold on quietly drained it. I wrote the full hands-on Descript review for the qualitative side — what the editing actually feels like, where Studio Sound earns its keep, and who should skip it — but the pricing is the part you model before you subscribe.

The free-plan landmine the model can't see

One more thing I only caught because I was logged in: on the Free plan, the Plan page calls your allowance "Monthly AI credits: 100," while the Usage page for the same account labels the identical balance "Lifetime AI credits used: 43/100." Those 100 credits are one-time. The product's own UI contradicts itself about whether they refill. If you are evaluating Descript on Free expecting a monthly reset that never arrives, that is a surprise no pricing model catches — you have to read the fine print in two places and notice they disagree.

Takeaways for anyone evaluating metered AI tools

Model two-meter pricing before you subscribe. When a tool bills on more than one axis, the binding constraint is rarely the one the marketing leads with. Here it is credits, not minutes.
Treat undocumented numbers as ranges, not values — and sweep them. This page has two: the per-action credit cost and whether the "+bonus" recurs. If your tier flips across plausible values of either, you do not have a budget, you have a hope.
Read a "+bonus" as marketing until proven recurring. Model the standing allowance; treat any bonus as upside you confirm on your second invoice, not capacity you plan around.
Price your heaviest month, not your average. No-rollover meters punish spiky usage.
Spend allowances are not refunds. Media-minutes-on-upload means a sloppy import is gone whether or not you use the footage.

The model is ~130 lines of stdlib — fork the Workflow numbers to your own cadence and it will tell you which tier you are really on, under both allowance assumptions. And if you have fought a worse two-meter pricing model than this one, I would genuinely like to read about it in the comments.

Wiring the ElevenLabs API into a real pipeline: the SDK is 4 lines, the billing isn't

Tien Nguyen — Tue, 09 Jun 2026 04:26:17 +0000

The ElevenLabs quickstart is four lines and it works on the first run. That part is honest. What the quickstart does not tell you is that the two things most likely to break your integration in production are not in the code at all — they are how the API bills you, and how it hands you a voice that quietly stops existing.

I run ElevenLabs behind a content pipeline that generates voiceovers for short-form video. Not a toy script — a service that calls the API on a schedule, caches the audio, and ships it downstream. Here is what I wish someone had told me before I wired it in.

The happy path really is this short

from elevenlabs.client import ElevenLabs

client = ElevenLabs(api_key="YOUR_API_KEY")

audio = client.text_to_speech.convert(
    voice_id="JBFqnCBsd6RMkjVDRZzb",      # a library or cloned voice
    model_id="eleven_multilingual_v2",
    text="Welcome to the show.",
    output_format="mp3_44100_128",
)

convert returns an iterator of bytes, so you write it out like any stream:

with open("out.mp3", "wb") as f:
    for chunk in audio:
        f.write(chunk)

That is the whole text-to-speech call. The same API key and the same credit pool drive this and the web app, which is the part I actually care about as a developer: what I prototype in the browser is what ships. No second account, no separate quota.

Gotcha 1: a `voice_id` is not a stable identifier

This one cost me a confusing afternoon. I hardcoded a voice_id I had picked from the Voice Library. A few weeks later the pipeline started throwing on that ID. The voice had been removed from the library by whoever published it, and the ID went with it.

The library is community-contributed. Voices come and go. If your code references a library voice by ID, you have a dependency on someone else's decision to keep sharing it.

Two fixes, depending on how much you care:

Add the voice to your own collection from the dashboard (the "Add to My Voices" button on the voice), which drops it into your My Voices list with a stable ID so a library removal does not pull it out from under you.
For anything load-bearing, clone or design the voice so the ID is yours. A designed voice generated from a text prompt is reproducible and never disappears.

Either way: do not treat a raw library voice_id as a permanent key. Treat it like a CDN URL you do not control.

Gotcha 2: streaming is a different method, and you want it for agents

convert waits for the full clip before it hands you bytes. Fine for batch voiceover. Wrong for anything interactive, where time-to-first-byte is the number that matters.

For a realtime voice agent, use the streaming endpoint and a low-latency model so audio starts playing while the rest is still generating:

stream = client.text_to_speech.stream(
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_flash_v2_5",         # low-latency model for realtime
    text="Hold on, let me check that for you.",
    output_format="mp3_44100_128",
)

for chunk in stream:
    play(chunk)                            # pipe to your audio sink as it arrives

The mistake I see people make is benchmarking an agent with convert and concluding the latency is bad. It is not the model — it is that you asked for the whole file. Switch to stream, drop to the flash model, and the felt latency falls off a cliff.

Gotcha 3: you are billed per character, including the takes you throw away

Here is the one that actually shapes your architecture. ElevenLabs meters in credits, and credits map roughly one-to-one to characters of text. About 1,000 characters is a minute of audio.

The trap is that every generation is billed, including regenerations. During development I would tweak a script, re-run, tweak, re-run — and each of those runs spent real credits even though I was throwing the audio away. On a tool where the headline plan advertises ~121 minutes a month, a chatty dev loop or a busy agent eats that faster than the minute count suggests, because the count assumes you nail every take once. You will not, and neither will your users.

This turns into two concrete engineering decisions:

Cache aggressively, keyed on the inputs. If the (text, voice_id, model_id, settings) tuple is unchanged, the output is identical — so do not pay for it twice. A content hash of those inputs makes a clean cache key:

import hashlib

def cache_key(text, voice_id, model_id, settings):
    blob = f"{text}|{voice_id}|{model_id}|{settings}".encode()
    return hashlib.sha256(blob).hexdigest()

Look that up before you call the API. In my pipeline this is the difference between paying once per script and paying every time a downstream retry fires.

Proof the text before it ever hits the API. Spell-check, normalize, lock the script as text first. Generating audio to discover a typo means paying to hear your own mistake read aloud, then paying again for the fix.

I went deeper on the credit economics — what the tiers actually buy, where the real per-minute ceiling lands once you account for regenerations, and whether it is worth it versus the cheaper options — in a full hands-on review of ElevenLabs where I ran my own scripts through every plan. The short version for builders: the API is excellent and the voices are a real class above the field, but price your expected traffic against the credit math, not the marketing minutes, before you commit a production workload to it.

So is the API worth building on?

For anything where the voice is the product — narration at scale, a voice agent, dubbing — yes, and it is not close. The SDK is clean, the Python and JavaScript clients are first-class rather than an afterthought, and the prototype-to-production continuity is genuinely rare in this space.

Just go in knowing the three things the quickstart leaves out: pin your voices, stream for latency, and budget credits like the line item they are. Get those right up front and the integration is boring in the best way.

If you have wired up a different TTS API and hit a worse or better version of the billing problem, I would like to hear it — drop it in the comments.

5 Reasons Why React Developers Should Learn SolidJS

Tien Nguyen — Sun, 26 Feb 2023 16:04:19 +0000

As a React developer, you may have heard about SolidJS, a relatively new web framework that has been gaining popularity among developers in recent years. SolidJS provides a fresh take on building user interfaces, and many developers are curious to know more about it.

In this post, we'll take a look at five reasons why React developers should learn SolidJS. By the end of this article, you'll have a better understanding of how SolidJS can benefit your development workflow and why it's worth adding to your toolset.

Familiar Syntax

One of the most significant advantages of SolidJS is its familiar syntax, which is similar to React. If you're already familiar with React, you'll find SolidJS easy to learn and adopt. SolidJS uses JSX, which allows you to write HTML-like syntax within your JavaScript code. This makes it easy to write and understand your application's structure.

SolidJS also offers a similar component-based architecture to React, making it easy to break down your application into reusable and composable components. With this familiarity, you can quickly start building applications in SolidJS without much difficulty.

Reactive Programming

SolidJS offers a reactive programming model that makes it easier to handle complex state changes in your application. With SolidJS, you can declare reactive state variables that automatically update the view when they change. This reactive programming model simplifies the management of complex state changes, allowing you to focus on building your application's features.

Built-in State Management Solution

SolidJS provides built-in solutions for state management, which can make your application development process faster and more efficient. SolidJS offers two main options for managing state including Signals and Stores, which provide an intuitive way to manage state without the need for external libraries.

In contrast, React doesn't have a built-in solution for state management, and you have to use a state management library such as Redux, Zustand, or MobX. While these libraries can provide powerful and flexible state management solutions, they also add complexity to your codebase and require additional setup and configuration.

Faster Performance

SolidJS is faster than React because it uses a system called fine-grained reactivity to write updates directly onto a webpage, while React updates a virtual DOM with its page changes. The reactivity system enables SolidJS to update only what changes, while React's virtual DOM re-renders entire components.
As I mentioned in my recent article about SolidJS vs Svelte, SolidJS beats React and almost every other JavaScript library out there in terms of performance. According to the JS Framework Benchmark, SolidJS is about 5% slower than vanilla JS, while React is at best almost 100% slower.

Smaller Bundle Size

Like Svelte, SolidJS compiles to vanilla JavaScript at build stage. This means that it does not need any runtime libraries or dependencies to run on browsers. SolidJS eliminates the need for a virtual DOM, which is a critical component of React's architecture. With SolidJS, you can create highly performant web applications with minimal bundle sizes, which can lead to faster load times and a better user experience.

Conclusion

In summary, SolidJS is a powerful web framework that offers many advantages for React developers. Its familiar syntax, faster performance, smaller bundle size, reactive programming model, and built-in state management solution make it an excellent choice for building high-performance web applications.

By learning SolidJS, you can add a new tool to your development workflow, expanding your options and capabilities. If you're interested in learning SolidJS, there are many resources available online, including the official documentation and community-driven tutorials. So why not give SolidJS a try and see how it can benefit your next web application?

A Beginner's Guide to Props and State in React

Tien Nguyen — Tue, 21 Feb 2023 04:27:21 +0000

React is a robust and popular JavaScript library for building dynamic and interactive user interfaces. One of the core concepts in React development is understanding the difference between props and state and using them effectively in your applications. In this blog post, we'll explore what React props and state are, how they differ, and best practices for using them.

If you're new to React development and wondering if it's straightforward to learn, I recommend checking out my blog post Is React easy to learn?. In this post, I point out the common challenges faced by beginners and provide some tips and resources to help you get started with React.
Now, let's dive into the world of React props and state and learn how they can be used to create rich and dynamic user interfaces.

What are React props?

React props are short for "properties," which are used to transfer data between different components. They allow you to customize a component by providing values to its attributes. In simple terms, props are like function arguments in JavaScript or parameters in other programming languages.

A component can receive props from its parent component and use them to render dynamic content. You can pass any type of data as props, including numbers, strings, objects, arrays, and even functions.

To pass props to a component, you define them as key-value pairs in the component's opening tag. For example, suppose you have a component called "Button" that you want to customize with different text and color. You can pass these values as props like this:

<Button text="Click me!" color="blue" />

In this example, the props are "text" and "color," and their values are "Click me!" and "blue," respectively. The component can then use these values to render a button with the specified text and color.

Props are read-only, meaning that a component can't modify the values of its props. This is to ensure that data flows in a single direction, from parent to child components. If you need to modify a value, you should use React state instead, which we'll cover in the next section.

What is React state?

React state is another essential feature of React components. Unlike props, which are passed down from a parent component, state is a component's internal data that can change over time. State allows you to build dynamic and interactive components that respond to user input, data changes, and other events.

To define and use state in a component, you need to declare it in the component's constructor function. For example, suppose you have a component called "TextInput" that allows a user to input text and displays the input in real-time. You can define the initial state of the component in its constructor like this:

class TextInput extends React.Component {
  constructor(props) {
    super(props);
    this.state = { value: "" };
  }

  handleChange(event) {
    this.setState({ value: event.target.value });
  }

  render() {
    return (
      <div>
        <input type="text" value={this.state.value} onChange={this.handleChange.bind(this)} />
        <p>You typed: {this.state.value}</p>
      </div>
    );
  }
}

In this example, the initial state of the component is an object with a single property called "value," which is set to an empty string. The component's render method displays an input field and a paragraph element that shows the input in real-time. The handleChange method updates the component's state using the setState method, which takes an object that defines the new state based on the user input.

React state is meant for internal component state only and should not be used to store global data or shared state between components. In such cases, you should use other solutions such as Redux or Context API.

In addition to the traditional method of handling state using the class-based components, React 16.8 introduced a new feature called the useState hook. The useState hook allows functional components to handle state without the need for a class. It's a more straightforward and concise way to manage state, reducing the complexity of state handling in React applications. This approach can make code more manageable and easier to understand, especially for developers who are new to React.

Understanding the difference between props and state

React props and state are both fundamental concepts that play a crucial role in building dynamic and interactive user interfaces. However, they differ in several ways, and understanding these differences is crucial to building efficient and scalable React applications.

Here are the main differences between props and state:

Source of data
The primary difference between props and state is their source of data. Props are passed down from a parent component to its child component, while state is defined and managed internally by a component.

Mutability
Another significant difference is the mutability of props and state. Props are read-only, and a child component cannot modify the props it receives from its parent. On the other hand, state is mutable, and a component can update its state using the setState() method.

While props and state serve different purposes, they are not mutually exclusive. Components can use both props and state to manage their data and provide an optimal user experience.

Best practices for using props and state in React

Now that we have a good understanding of React props and state and how they differ, let's discuss some best practices for using them in your React applications.

Keep props and state simple
It's important to keep your props and state as simple as possible. Avoid storing unnecessary data or logic in your state or passing down too many props to your child components. Instead, try to break down your application into smaller and more manageable components, each with its own clear responsibilities.

Use props for data that doesn't change
Props are ideal for data that doesn't change throughout the lifecycle of a component. This includes data such as configuration settings, static content, and user information.

Use state for data that changes
On the other hand, state is suitable for data that changes over time, such as form inputs, user interactions, or data fetched from an API. It's important to update the state using the setState() method and avoid directly mutating the state object.

Avoid unnecessary rerendering
Rerendering can be expensive, so it's important to avoid unnecessary rerendering of your components. An approach to accomplish this is to use the shouldComponentUpdate() lifecycle method to determine whether a component needs to be rerendered or not. Another way is to use the React.memo higher-order component to memoize the component and prevent unnecessary rerendering.

Use functional components and hooks
Functional components and hooks are new features in React that provide a simpler and more concise way of defining and managing components. They also eliminate the need for class components and the this keyword, making it easier to reason about your code and avoid common pitfalls.

Conclusion

React props and state are essential concepts for constructing responsive and engaging user interfaces within React applications. Understanding these concepts and using them effectively can help you build scalable and efficient React applications. We have covered what React props and state are, how they differ, and best practices for using them.

If you're looking to learn React fast, I suggest reading my blog post on how to learn React JS quickly. In this post, I provide some tips and resources to help you get started with React and accelerate your learning.

If you're looking to take your React skills to the next level, check out the blog post about how to master React JS. This post provides more advanced techniques and strategies for building complex and interactive user interfaces with React.

By mastering the concepts of React props and state and continually improving your React skills, you can build high-quality and performant applications that meet the needs of your users.

Getting Started with Material UI in React: An Introduction

Tien Nguyen — Mon, 20 Feb 2023 08:48:12 +0000

Material UI is a popular UI library for React that provides pre-designed components following Google's Material Design principles. It offers a wide range of tools to make it easy for developers to create beautiful, modern user interfaces. In this post, I will introduce you to Material UI and guide you through the steps to get started with it in your React project.

Installation

Before you can start using Material UI in your React project, you need to install it. You can do this by running the following command in your terminal:

npm install @mui/material @emotion/react @emotion/styled
// or
yarn add @mui/material @emotion/react @emotion/styled

This command will install the core components of Material UI in your project.

Usage

Once you have installed Material UI, you can start using its components in your React project. You need to import the component you want to use from the Material UI library and then use it in your JSX code.

Here is an example of how to use the Button component from Material UI:

import React from 'react';
import Button from '@mui/material/Button';

function App() {
  return (
    <Button variant="contained" color="primary">
      Click me
    </Button>
  );
}

export default App;

In this example, we import the Button component from the Material UI library and use it in the JSX code of our functional component. We set the variant and color props to customize the button's appearance.

Theming

Material UI also provides theming options to allow you to customize the look and feel of your app easily. You can define your custom theme using the createTheme function, which returns a theme object that you can customize.

Here is an example of how to customize the primary and secondary colors of your Material UI theme:

import { createTheme } from '@mui/material/styles';

const theme = createTheme({
  palette: {
    primary: {
      main: '#ff4081',
    },
    secondary: {
      main: '#3f51b5',
    },
  },
});

After defining the theme, you can pass it to your app using the ThemeProvider component, like this:

import React from 'react';
import { ThemeProvider, createTheme } from '@mui/material/styles';
import Button from '@mui/material/Button';

const theme = createTheme({
  palette: {
    primary: {
      main: '#ff4081',
    },
    secondary: {
      main: '#3f51b5',
    },
  },
});

function App() {
  return (
    <ThemeProvider theme={theme}>
      <Button variant="contained" color="primary">
        Click me
      </Button>
    </ThemeProvider>
  );
}

export default App;

In this example, we wrap our app in the ThemeProvider component and pass the theme object to it as a prop. This will apply the custom theme to all Material UI components in our app.

Conclusion

In conclusion, Material UI is an excellent choice for React developers who want to build modern and visually appealing user interfaces. Its vast selection of pre-designed components, easy-to-use theming options, and simplicity of use make it a powerful tool for creating amazing web applications.

If you want to explore another popular UI library for React, you might be interested in reading our blog post comparing Material UI vs Ant Design. In this post, I compare and contrast the features of both libraries, so you can choose the best one for your project. Happy coding!

Building Responsive Web Apps with Tailwind CSS

Tien Nguyen — Mon, 20 Feb 2023 05:45:08 +0000

Learn how to build responsive web apps with Tailwind CSS, a powerful and flexible CSS framework that streamlines your design process and makes it easy to create mobile-first, responsive web pages.

Introduction

In today's digital age, building responsive web apps is a must. With the rise of mobile devices, it's more important than ever to create web pages that look great on all screen sizes. That's where Tailwind CSS comes in. Tailwind CSS is a popular CSS framework that makes it easy to build responsive web apps. In this article, we'll explore the basics of Tailwind CSS and how you can use it to create stunning, responsive web apps.
If you're interested in comparing Tailwind CSS to another popular CSS framework, Chakra UI, be sure to check out our blog post on Chakra UI vs Tailwind to see which one is best for your next project.

What is Tailwind CSS?

Tailwind CSS is a utility-first CSS framework that allows you to quickly design and build responsive web apps. Unlike traditional CSS frameworks that rely on predefined classes and styles, Tailwind CSS provides a set of utility classes that you can use to style your HTML elements. This makes it easy to create custom designs without writing a lot of custom CSS.

Getting Started with Tailwind CSS

To get started with Tailwind CSS, you'll need to install it using a package manager such as npm or yarn. Once you have it installed, you can start using Tailwind CSS classes in your HTML code. Here are some of the basic classes that you'll need to know:

container: creates a container that centers your content and adds padding
mx-auto: centers an element horizontally
text-center: centers text
bg-gray-200: sets the background color to gray These are just a few of the many classes that you can use with Tailwind CSS. By combining these classes, you can create a variety of layouts and designs.

Building a Responsive Web App with Tailwind CSS

To build a responsive web app with Tailwind CSS, you'll need to follow some basic principles. Here are some tips to help you get started:

Start with a mobile-first approach: This means designing your web app for mobile devices first, and then scaling up to larger screens. This approach ensures that your web app will look great on all screen sizes.
Use responsive classes: Tailwind CSS provides a set of responsive classes that allow you to apply different styles based on screen size. For example, you can use the sm:text-lg class to increase the font size on small screens.
Use flexbox: Flexbox is a powerful layout tool that allows you to create flexible and responsive layouts. Tailwind CSS provides a set of flexbox classes that make it easy to create complex layouts.
Test on multiple devices: To ensure that your web app looks great on all screen sizes, you'll need to test it on multiple devices. Use tools such as Chrome DevTools to simulate different screen sizes and test your web app's responsiveness.

FAQs

Q: Is Tailwind CSS easy to learn?
A: Yes, Tailwind CSS is relatively easy to learn. Its utility-first approach makes it easy to style HTML elements using pre-defined classes.

Q: Can I use Tailwind CSS with other CSS frameworks?
A: Yes, you can use Tailwind CSS with other CSS frameworks. However, it is not recommended as it can lead to conflicts and increase the file size of your web app.

Q: Does Tailwind CSS slow down my web app's performance?
A: No, Tailwind CSS is designed to be fast and lightweight. It uses a build process that removes unused CSS, so you only include the styles that you need.

Conclusion

Tailwind CSS is a powerful and flexible CSS framework that makes it easy to build responsive web apps. With its utility-first approach and responsive classes, you can create stunning designs that look great on all screen sizes. By following the basic principles of responsive design and using the right tools, you can create

Svelte vs Vue.js: Key Differences and Considerations

Tien Nguyen — Wed, 04 Jan 2023 13:49:07 +0000

With a sea of JavaScript frameworks to choose from, it can be difficult for developers to make the best choice. Svelte and Vue.js are two popular frameworks that stand out due to their simplicity and performance — making them ideal candidates for building fast web applications. But before you decide which is better suited for your project, there are crucial differences between these two options that you must consider.

Before we look at the comparison between Svelte and Vue.js, let's not forget that Next.js is often associated with them. If you want to learn more about Svelte vs Next JS, click on the link to check it out.

Introduction to Svelte

Now, let's take a look at Svelte. Svelte is a relatively new framework that was released in 2016. With its simple and minimalistic design, Svelte stands out as the ideal lightweight alternative to other JavaScript frameworks like React and Angular.

One of the main advantages that Svelte provides is its compiler; this compiles components into standard JavaScript, eliminating any need for a runtime. This feature makes Svelte applications incredibly fast and efficient, as there is no requirement for extra code to be interpreted by the browser when running your app.

Svelte stands out for its minimalistic approach, from the lightweight API and concise syntax that makes it simple to learn and get started with the framework. Additionally, Svelte's reactive components make building interactive and responsive user interfaces easy.

Introduction to Vue.js

Now, let's explore Vue.js. Vue.js is an innovative JavaScript framework created by Evan You in 2014, which was crafted to be lightweight and user-friendly with a small API and straightforward syntax.

It is a perfect tool for creating dynamic, interactive user experiences through its reactive components.

Similar to Svelte, Vue.js is built with an equal focus on being lightweight and performance; however, it does require the use of a virtual DOM in runtime – meaning that in certain circumstances, it could be slower than Svelte. Fortunately, some performance optimizations available for Vue.js can help reduce its impact on speed.

Key Differences Between Svelte and Vue.js

Svelte and Vue.js have different approaches when it comes to reactivity. What sets Svelte apart is its inbuilt mechanism, meaning components automatically refresh when the underlying data is changed. There's no need for special syntax or declaring reactive information, which simplifies the development of interactive applications.

On the other hand, Vue.js is built on a reactive system derived from "data binding." This means that developers must define precisely which data should be reactive and utilize special syntax to link it with the DOM element. Although this can require more effort in advance, it will allow greater control over reactivity and data flow throughout your codebase.

Another difference between Svelte and Vue.js is how they handle state management. As the size of your application grows, so does its complexity and data flow management. Svelte makes it easier to take control of state management at a component level for small to medium-sized applications. Still, things can quickly become complicated when an app exceeds this scope.

In contrast, Vue.js offers some valuable options for state management by incorporating the Vuex library, which is developed to provide a centralized repository for application data. This can make managing large and intricate applications more efficient by delivering distinct divisions between responsibilities and allowing consistent access and updates throughout the entire program.

Vue.js may be the more popular programming framework and has a larger, vibrant community, which is of great benefit for developers who are just starting with it - there are plenty of resources and support available online. Nevertheless, Svelte's community is also quickly growing, earning its reputation as being easy to learn while still providing an extensive amount of features.

Use Cases for Svelte and Vue.js

When choosing between Svelte and Vue.js, it can be challenging to decide which framework best suits your project's needs. To make this decision easier, here are some key points to consider:

If you are looking for an uncomplicated framework with a concise API, Svelte or Vue.js could be the perfect solution for your needs.
If you need an efficient and lightweight framework, Svelte might be the better choice.
If you need an excellent framework to create engaging and responsive user interfaces, either Svelte or Vue.js is the ideal option.
If you need a framework with a large and active community, Vue.js might be the way to go.

Conclusion

In the end, selecting between Svelte and Vue.js will come down to your project's needs and objectives. Both frameworks possess distinct advantages that you should consider before making your decision; as a result, it is highly recommended that you evaluate both options thoroughly before deciding which one best suits your purposes.

DEV Community: Tien Nguyen

I run a 209-node automation pipeline on n8n. I modeled what it would cost on per-task billing.

The three tools bill on three different units

The model

Now scale it to the whole pipeline

The honest other side

The takeaway

Two opposite designs for AI meeting notes: transcribe everything vs enhance what you typed

Two models

What that looked like on one meeting

Why the input model decides the output

The decision, and the build lesson

You can't benchmark an AI notetaker against a real meeting — you don't know the right answer. So I generated the meeting.

Generate the meeting, keep the answer key

The surprise: clean-audio accuracy is a wash

What the synthetic-ground-truth trick is good for (and not)

Murf bills AI voiceover by the second, ElevenLabs by the character. I did the arithmetic to find where each one wins.

Two meters

The break-even is a speaking density

Pauses are the punchline

Takeaways for anyone comparing metered tools

Descript bills you on two meters at once. I modeled it in Python to find which plan actually fits.

Two meters, not one

The plans, as data

Describing a month of work

Running it: the answer moves

The free-plan landmine the model can't see

Takeaways for anyone evaluating metered AI tools

Wiring the ElevenLabs API into a real pipeline: the SDK is 4 lines, the billing isn't

The happy path really is this short

Gotcha 1: a voice_id is not a stable identifier

Gotcha 2: streaming is a different method, and you want it for agents

Gotcha 3: you are billed per character, including the takes you throw away

So is the API worth building on?

5 Reasons Why React Developers Should Learn SolidJS

Familiar Syntax

Reactive Programming

Built-in State Management Solution

Faster Performance

Smaller Bundle Size

Conclusion

A Beginner's Guide to Props and State in React

What are React props?

What is React state?

Understanding the difference between props and state

Best practices for using props and state in React

Conclusion

Getting Started with Material UI in React: An Introduction

Installation

Usage

Theming

Conclusion

Building Responsive Web Apps with Tailwind CSS

Introduction

What is Tailwind CSS?

Getting Started with Tailwind CSS

Building a Responsive Web App with Tailwind CSS

FAQs

Conclusion

Svelte vs Vue.js: Key Differences and Considerations

Introduction to Svelte

Introduction to Vue.js

Key Differences Between Svelte and Vue.js

Use Cases for Svelte and Vue.js

Conclusion

Gotcha 1: a `voice_id` is not a stable identifier