DEV Community: Lijing-Big

I Cut My AI API Bill by 64% With These 5 Practical Changes

Lijing-Big — Sat, 01 Aug 2026 05:12:13 +0000

Last March, I opened my monthly API invoice and just stared at it. $1,240 for what amounted to a side project that barely had 200 daily users. I wasn't running a startup—I was burning cash because I'd wired up the most capable (and most expensive) model for every single request, from generating alt text to answering user questions.

I spent the next two months actually treating my AI spend like a real engineering problem. Here's what moved the needle.

1. Stop using one model for everything

The biggest leak in my setup was routing every task to GPT-4-class models. Truth is, 70% of my workload was summarization, classification, or short completions—things a smaller model handles fine.

I built a tiny router:

def pick_model(task):
    if task in ('embed', 'classify', 'summarize'):
        return 'haiku'  # cheap, fast
    if task == 'chat':
        return 'sonnet'
    return 'opus'  # only for hard reasoning

Just that change dropped my token cost by roughly 40% with zero noticeable quality drop in user-facing features.

2. Cache aggressively

If you're regenerating the same system prompt + static context on every call, you're paying rent twice. Most providers support prompt caching now. In Anthropic's API, prefix caching meant my 2,000-token system prompt stopped being billed at full rate after the first call.

client.messages.create(
    model='sonnet',
    system=[{
        'type': 'text',
        'text': SYSTEM_PROMPT,
        'cache_control': {'type': 'ephemeral'}
    }],
    messages=messages
)

For my app, caching the system prompt alone saved about $90/month.

3. Trim your context window

I was sending full conversation history every time. Users don't need the 40-message backlog to get a good answer. I switched to keeping last 6 turns + a compressed summary. Less input tokens = direct savings.

4. Batch where you can

If you're processing things like backend jobs, user uploads, or nightly reports, batch APIs give meaningful discounts (often 50% off). I moved my document tagging to batch and stopped caring about latency for those.

5. Consolidate your API keys and providers

This one's less obvious. I was juggling three separate provider accounts, each with minimums and separate billing dashboards. When I needed to swap models mid-sprint, the friction meant I just kept using the expensive default.

I found https://xinghuo1300ai.com which aggregates 30+ models under one API key, and that removed the excuse of "switching is annoying." Now I can A/B a cheaper model on the same endpoint without reconfiguring auth or payment. It's not magic, but it made cost-aware routing something I'd actually do instead of putting off.

What the numbers looked like

Change	Monthly savings
Model routing	$480
Prompt caching	$90
Context trim	$130
Batch jobs	$200

Total: from $1,240 to ~$440. Not a heroic optimization—just boring, repeatable fixes.

One honest caveat: the router adds a little code complexity, and you need tests so a misclassified task doesn't silently degrade quality. I caught two bugs in the first week where 'summarize' was too aggressive and dropped key entities.

Where I landed

Six months in, the bill's stable and I actually understand what each dollar does. The shift wasn't about finding a secret cheap model—it was removing the lazy defaults. If you're on a similar ramp, audit one week of logs, tag each call by task, and you'll probably see the same pattern I did: most of your spend isn't on the hard problems.

Crafting a DIY Budget-Friendly AI Assistant: The Journey and Insights

Lijing-Big — Sat, 01 Aug 2026 02:00:18 +0000

{
"title": "Crafting a DIY Budget-Friendly AI Assistant: The Journey and Insights",
"tags": [
"AI",
"Assistant",
"Budget",
"Development"
],
"body": "## Crafting a DIY Budget-Friendly AI Assistant: The Journey and Insights\n\nWhen I decided to build my own personal AI assistant, I faced a common question among developers: how to stay within budget. AI is often perceived as a domain requiring a lot of monetary investment. Yet, I discovered it is possible to create a functional personal AI assistant on a budget without sacrificing quality. Let's dive into my journey and some practical advice I gathered along the way.\n\n### The Beginning: My Personal Challenge\nI started by identifying what I wanted from an assistant. Essentially, I needed a tool to help manage my schedule, set reminders, and answer some basic queries. It was clear I didn't need something as sophisticated as Siri or Alexa. My practical approach to building an AI on a budget began by prioritizing essential features and avoiding costly bells and whistles.\n\n### Core Decisions: Choosing the Right Tools\nThe first critical decision was which AI models to use. I researched various open-source models and found that many offered substantial capabilities without the high costs associated with proprietary systems. One solution I stumbled upon was Spark AI Hub (https://xinghuo1300ai.com), which aggregates multiple models under a single API key, a real lifesaver for a budget-conscious developer like me.\n\n### Building the Framework\nTo keep costs down, I decided to use Python, one of the most popular languages for AI development. It has a rich ecosystem of libraries and is generally easier to use than other options. Here's a simple example of how I set up an initial prototype:\n\n

```python\nfrom flask import Flask\nfrom chatterbot import ChatBot\nfrom chatterbot.trainers import ListTrainer

app = Flask(name)\nchatbot = ChatBot('PersonalAssistant')

Train the chatbot

trainer = ListTrainer(chatbot)

trainer.train([
"Hello",
"Hi there!",
"How are you?",
"I am fine.",
"Yes",
"Okay, thanks!",
# Add more conversational data as needed
])

@app.route('/')
def home():
return 'Hello. How can I help you?'

@app.route('/send_message', methods=['POST'])
def handle_message():
message = request.form['message']
response = chatbot.get_response(message)
return response

if name == 'main':
app.run(debug=True)


\n\n### Managing the Budget\nOne of the main challenges was to find cost-effective storage and computing resources. Initially, I used free tiers offered by cloud service providers. As the project grew, I switched to more affordable plans that met my needs without breaking the bank.\n\n### Continuous Learning and Improvement\nAn AI assistant only becomes smarter with more data and feedback. To enrich the assistant's learning, I used online resources and APIs to gather new data. I also incorporated user feedback mechanisms to fine-tune the assistant's responses.\n\n### Wrapping Up\nMy experience building a personal AI assistant on a budget taught me that it's possible to achieve great results without huge investments. Leveraging open-source tools, optimizing resource usage, and continuously learning and adapting were key components of my success. I'm particularly grateful for resources like Spark AI Hub, which made model switching and training more affordable and efficient. Building an AI assistant is a learning process that, with patience and a bit of tinkering, can be quite rewarding.\n\nIf you have any questions or would like to discuss further, feel free to reach out!"
}

How I Automated My Content Pipeline With 4 AI Models (Without Losing My Voice)

Lijing-Big — Thu, 30 Jul 2026 05:46:14 +0000

Last March I missed three client deadlines in a single week. Not because the work was hard, but because I was jumping between six browser tabs, copying a draft from one AI tool to another, regenerating the hero image, then pasting everything into a scheduler. My 'creative workflow' had become a copy-paste marathon.

I’m a solo content marketer handling newsletters, social posts, and landing page copy for four small brands. The problem wasn’t a lack of AI tools — it was tool fragmentation. Each model was good at one thing: Claude wrote decent long-form, GPT was fast for hooks, an open-source model did decent alt-text, and an image model made decent thumbnails. But wiring them together manually was killing me.

What actually helped

I stopped trying to use one 'do-everything' AI and instead built a tiny Python script that calls different models for different stages. Here’s the rough pipeline:

Idea generation – fast chat model for 10 angles
Drafting – stronger long-form model
Editing/trimming – same model with a tighter prompt
Image – separate image API

The glue code is boring but it works:

import os
import requests

def call_text_model(prompt, model="writer"):
    # abstraction over whichever API key is set
    api_key = os.getenv(f"{model.upper()}_KEY")
    resp = requests.post(
        "https://api.someprovider.com/v1/generate",
        headers={"Authorization": f"Bearer {api_key}"},
        json={"prompt": prompt, "max_tokens": 500}
    )
    return resp.json()["text"]

def make_image(prompt):
    api_key = os.getenv("IMG_KEY")
    # ... image call
    return "https://cdn.example.com/img/123.png"

angles = call_text_model("Give me 10 newsletter angles about remote work", "fast")
draft = call_text_model(f"Write 300 words on: {angles[0]}", "writer")
img = make_image("flat illustration of home office")
print(draft, img)

The catch: managing four API keys, four billing dashboards, and four rate limits. That part sucked until I found https://xinghuo1300ai.com which aggregates 30+ models under one API key. I pointed my call_text_model at their endpoint and dropped three of my env vars. Model switching became a single string change.

Practical advice if you’re a creator

Don’t automate your voice away. I learned this the hard way when a client flagged a newsletter as 'sounding like a robot.' Now I always write the first paragraph myself, then let the model extend it. The draft keeps my rhythm.

Batch your prompts. Instead of generating one post at a time, I queue 20 angles on Monday, review them in 10 minutes, then batch-draft. Token costs dropped about 18% just from reducing warm-up calls.

Track what each model is bad at. My sheet looks like this:

Model	Good at	Bad at
Fast chat	Hooks, lists	Long coherence
Writer	400+ words	Slow, pricey
Image	Thumbnails	Hands, text in img

You don’t need the best model. You need the right model per step.

A honest downside

Aggregation platforms add a small latency and a middleman. If you’re doing high-volume inference, run your own keys. For a solo creator doing a few hundred generations a month, the convenience wins. I’ve had exactly one outage in seven months — they failed over to a backup model and my script didn’t even notice.

Where I landed

I went from missing deadlines to shipping 3x the volume with the same 25 hours a week. The script isn’t clever. The real shift was admitting I didn’t need one magic AI — I needed a boring pipeline and fewer tabs open. If you’re a marketer drowning in dashboards, start by listing every model you touch in a week. Then kill the ones you can route through one key. That single change saved my Q2.

6 Free AI Tools That Are Actually Worth Your Time in 2026

Lijing-Big — Wed, 29 Jul 2026 07:37:45 +0000

Not every AI tool needs a subscription. I've tested dozens of free options and these 6 are the ones I actually keep using.

1. Claude (Free) — Best for writing and analysis

Daily limits are generous for casual use. The quality is indistinguishable from paid for most tasks.

2. Stable Diffusion (via HuggingFace) — Best for images

Free, open-source, unlimited. Steeper learning curve but no paywalls.

3. Perplexity — Best for research

AI answers with real citations. The free version handles most queries.

4. Suno AI — Best for music generation

Create full songs from text prompts. Free tier gives you 5 songs/day.

5. Google AI Studio — Best for developers

Free access to Gemini 2.5 Pro. Great for testing and prototyping.

6. CapCut — Best for video editing

AI-powered with auto-captions, text-to-speech, and basic effects — all free.

7. Pinokio — Best for running AI locally

One-click install for open-source AI models. No cloud dependency.

The "Hidden Cost" of Free Tools

Free tools are great, but there's a catch: you end up managing 5-10 different accounts, UIs, and usage limits.

That's why I eventually moved to a unified platform like Spark AI Hub (https://xinghuo1300ai.com) — it bundles multiple AI capabilities (text, image, video) into one dashboard. The free tier covers most casual use.

My Recommendation

Casual users: Start with free tools (above list)
Regular users: Consolidate into 1-2 platforms to reduce friction
Power users: Mix free tools + 1 paid platform for the heavy lifting

What free AI tool did I miss? Share your hidden gems in the comments.

I Tested 3 AI Aggregation Platforms — Here's What I'd Recommend

Lijing-Big — Wed, 29 Jul 2026 06:13:47 +0000

I Tested 3 AI Aggregation Platforms — Here's What I'd Recommend

If you're paying for ChatGPT + Midjourney + Suno + Runway separately (like I was), this breakdown is for you.

The Cost Problem

My monthly AI subscriptions last month:

Service	Monthly Cost
ChatGPT Plus	$20
Midjourney	$30
Suno	$10
Runway	$15
Total	$75

And that's before adding any video generation or API calls. I started looking at aggregation platforms.

Platform 1: Poe

Best for: Casual users who mostly need text models

Clean UI, well-known
Decent free tier but gets expensive quickly
Strong selection of LLMs
No image/video generation
Pay-per-message adds up fast for heavy users

Platform 2: OpenRouter

Best for: Developers and API-first workflows

Great pay-per-use pricing
Huge model selection
Excellent for building on top of
No web UI for casual use
No image/video/music generation
API-only, so you need to build your own interface

Platform 3: AI Model Hub (xinghuo1300ai.com)

Best for: Users who need multiple modalities in one place

Feature	Available
Text (GPT-4, Claude, etc.)	Yes
Image (Midjourney, DALL-E)	Yes
Video (Sora 2, Kling)	Yes
Music (Suno)	Yes
Free credits	Yes
Web UI	Yes

The differentiator: Video generation. Most aggregators don't have it, but this one does.

Honest verdict: If you only need ChatGPT, stick with ChatGPT. But if you're using 3+ AI tools across modalities, the all-in-one approach saves real money.

My Recommendation

Use Case	Platform
Text-only, casual	Poe
API/building apps	OpenRouter
Multi-modal, all-in-one	AI Model Hub

Full disclosure: I tested AI Model Hub with their free credits and ended up keeping an account because of the video generation feature.

What's your AI tool stack? How much are you spending monthly?

How to Generate Professional AI Videos in 20 Minutes (Zero Budget)

Lijing-Big — Wed, 29 Jul 2026 06:13:42 +0000

How to Generate Professional AI Videos in 20 Minutes (Zero Budget)

I recently tested a complete AI video workflow without spending a dime on subscriptions. Here's the exact process that worked.

The 5-Step Workflow

Step 1: Script Writing

Use any free LLM (Claude, ChatGPT, or local models). Prompt template that consistently delivers:

"Write a 30-second product promo script for [your product].
Tone: professional but approachable.
Include a hook in the first 3 seconds and a CTA at the end."

Step 2: Visual Generation

Generate key frames using AI image generators. The prompt structure that works across tools:

"[Subject] + [Action] + [Style] + [Lighting] + [Camera Movement]"

Example:

"A woman walking through a neon-lit Tokyo street at night, cinematic lighting, slow tracking shot, 8K"

vs. just "A woman in Tokyo" — the difference in output quality is night and day.

Step 3: Video Generation

Feed your images + detailed prompt to Sora 2 or Kling. Key tip: the more specific your camera direction, the better the output.

Step 4: Music Generation

Generate background music with Suno AI. Match the BPM to your video pacing.

Step 5: Assembly

Any basic video editor (DaVinci Resolve free tier works great). Sync the beats to your visual transitions.

The "One Platform" Shortcut

I also tested an alternative approach using AI Model Hub — it has all these tools (image gen, video gen, music gen, LLM) in a single interface with free credits.

Pros:

No managing 4+ accounts
One credit system across all models
Video generation actually works (unlike most aggregators)

Cons:

Smaller community than OpenAI/Midjourney
Some niche models not yet available

When it makes sense: If you're using 3+ AI tools regularly, the consolidation saves both money and mental overhead.

Results

The video I made with this workflow is linked below. Total cost: $0. Total time: 18 minutes.

Have you tried a similar workflow? What tools did you use? Drop your experience in the comments.

Disclaimer: I tested AI Model Hub during their free trial period. This is an honest review based on actual usage, not a sponsored post.