ByteByteGo - Community Posts- YouTube

Redis Data Structures Every Engineer Should Know - Strings store one value per key. They work for counters, session tokens, and cached payloads. - Hashes store an object's fields under one key. You can update one field without rewriting the rest. - Lists are ordered sequences with fast push and pop at both ends. They fit queues, feeds, and recent-item lists. - Sets hold unique members and support intersection, union, and difference. They cover tagging, follower overlap, and deduplication. - Sort...

ByteByteGo — Mon, 22 Jun 2026 16:56:49 GMT

Redis Data Structures Every Engineer Should Know

- Strings store one value per key. They work for counters, session tokens, and cached payloads.
- Hashes store an object's fields under one key. You can update one field without rewriting the rest.
- Lists are ordered sequences with fast push and pop at both ends. They fit queues, feeds, and recent-item lists.
- Sets hold unique members and support intersection, union, and difference. They cover tagging, follower overlap, and deduplication.
- Sorted Sets rank members by a numeric score. They handle leaderboards, priority queues, and top-N or range-by-score queries.
- Streams are an append-only log with consumer groups. Each consumer tracks its own position, and the server tracks unacknowledged messages.
- JSON stores nested documents with JSONPath access. You can update a field deep in a document without read-modify-write.
- Geospatial provides latitude/longitude indexes with radius and box queries. Under the hood it's a Sorted Set with geohash scores.
- Vector Set runs approximate nearest-neighbor search over embeddings. It's the retrieval step in most RAG pipelines.
- Time Series stores timestamped samples with built-in retention, downsampling, and labels. It fits metrics, telemetry, and IoT data.

Over to you: All ten are built-in as of Redis 8. Which one do you use most outside of caching?

--
Subscribe to our weekly newsletter to get a Free System Design PDF (368 pages): https://go.bytebytego.com/nl-subscribe

#systemdesign #coding #interviewtips
.

Single Agent vs. Multi-Agent Architecture Some tasks need a single agent. Others need a whole team. Knowing the difference is the skill. Single-agent system: One reasoning LLM that plans, picks a tool, and loops on its own until the task is done. Use a single agent when: - the task is a clear, linear sequence - one agent can hold the whole problem in its head - you want something simple to build and easy to debug Multi-agent system: An orchestrator that splits a task into subtasks and routes eac...

ByteByteGo — Sat, 20 Jun 2026 05:56:49 GMT

Single Agent vs. Multi-Agent Architecture

Some tasks need a single agent. Others need a whole team. Knowing the difference is the skill.

Single-agent system: One reasoning LLM that plans, picks a tool, and loops on its own until the task is done. Use a single agent when:
- the task is a clear, linear sequence
- one agent can hold the whole problem in its head
- you want something simple to build and easy to debug

Multi-agent system: An orchestrator that splits a task into subtasks and routes each one to a specialized agent. Use multi-agent when:
- subtasks can run in parallel
- one agent writes and another independently verifies the work
- the problem is too big for one agent to coordinate alone

Single agents are cheaper and easier to build, but they hit a ceiling on complex work.

Multi-agent systems are more capable and more reliable, but they add coordination cost.

Start with a single agent. Move to multi-agent only when context or reliability become the bottleneck.

Over to you: Are you running single-agent or multi-agent systems in production?

--
Subscribe to our weekly newsletter to get a Free System Design PDF (368 pages): https://go.bytebytego.com/nl-subscribe

#systemdesign #coding #interviewtips
.

Twelve models worth knowing in 2026, each with one standout strength. 1. Llama 4 Scout: Meta's first natively multimodal open-weight model. 2. DeepSeek V4: A Mixture-of-Experts model under MIT license with a native million-token context window. Near-frontier performance at a fraction of the cost per token. 3. Qwen3: Alibaba's flagship open-weight model with switchable thinking and non-thinking modes, all under Apache 2.0. 4. Gemma 4: Google's open-weight family released under Apache 2.0, with th...

ByteByteGo — Wed, 17 Jun 2026 05:56:49 GMT

Twelve models worth knowing in 2026, each with one standout strength.

1. Llama 4 Scout: Meta's first natively multimodal open-weight model.

2. DeepSeek V4: A Mixture-of-Experts model under MIT license with a native million-token context window. Near-frontier performance at a fraction of the cost per token.

3. Qwen3: Alibaba's flagship open-weight model with switchable thinking and non-thinking modes, all under Apache 2.0.

4. Gemma 4: Google's open-weight family released under Apache 2.0, with the widest language coverage of any model on this list.

5. Phi 4: Microsoft’s compact model trained almost entirely on synthetic, curated data. A practical choice for edge and on-device deployment.

6. Mistral Small 3.1: A VLM with a long context window that fits on a consumer laptop.

7. Nemotron 3 Super: NVIDIA’s hybrid MoE with a million-token context window. Fully open weights, datasets, and recipes, with strong results on agentic coding benchmarks.

8. GLM 5.1: The first open-weight model to top SWE-Bench Pro. Released under MIT with no commercial restrictions.

9. Kimi K2.6: Competitive with leading closed models on coding while costing far less per million tokens. Available on Hugging Face under a Modified MIT license.

10. StarCoder2: One of the most transparent code models available.

11. OLMo 2 (AI2): The most complete example of open-source reproducibility on this list. Weights, training data, code, and full recipes all released under Apache 2.0.

12. Falcon 3: A family of lightweight open-weight models built to run on a single GPU.

Over to you: which open-source model would you add to this list?

--
Subscribe to our weekly newsletter to get a Free System Design PDF (368 pages): https://go.bytebytego.com/nl-subscribe

#systemdesign #coding #interviewtips
.

SLMs vs. LLMs, Clearly Explained Big models cost more. Small models do less. Here's how SLMs and LLMs differ across the dimensions that matter in production: 1. Architecture: SLMs are usually under 10B parameters and run on a laptop or phone. LLMs sit at 10B+ with deeper layers and more attention heads, built for broad reasoning across tasks. 2. Task Complexity: SLMs work well on simple tasks but fail on complex multiple reasoning steps. LLMs handle difficult math, multi-step code, and long-hori...

ByteByteGo — Tue, 16 Jun 2026 05:56:49 GMT

SLMs vs. LLMs, Clearly Explained

Big models cost more. Small models do less. Here's how SLMs and LLMs differ across the dimensions that matter in production:

1. Architecture: SLMs are usually under 10B parameters and run on a laptop or phone. LLMs sit at 10B+ with deeper layers and more attention heads, built for broad reasoning across tasks.

2. Task Complexity: SLMs work well on simple tasks but fail on complex multiple reasoning steps. LLMs handle difficult math, multi-step code, and long-horizon planning.

3. Long Context Recall: SLMs lose the thread across long documents or extended conversations. LLMs reliably track and connect information across large inputs.

4. Latency and Cost: SLMs run on consumer hardware with low response times and significantly lower inference costs. LLMs require GPU and carry higher costs per request.

5. Deployment and Privacy: SLMs run on-device or on-premise. LLMs are typically cloud-hosted, which adds data governance complexity.

6. Where each fits:
SLMs: on-device assistants, real-time classification, or privacy-sensitive applications
LLMs: complex reasoning, agent workflows, or broad knowledge tasks.

Are you using SLMs, LLMs, or a hybrid setup in production?

--
Subscribe to our weekly newsletter to get a Free System Design PDF (368 pages): https://go.bytebytego.com/nl-subscribe

#systemdesign #coding #interviewtips
.

The Typical AI Agent Stack, Explained Most people think an AI agent is just a clever prompt and an LLM. The reality is much deeper. There's an entire architecture working behind the scenes to make it all run. The diagram below shows the full AI Agent Stack. At the core is the Agent Runtime that runs a ReAct loop, and three other layers feed into it. AI Agent Runtime: The LLM thinks about what to do, picks a tool, observes the result, then reflects and decides the next step. This loop repeats unt...

ByteByteGo — Fri, 12 Jun 2026 05:56:49 GMT

The Typical AI Agent Stack, Explained

Most people think an AI agent is just a clever prompt and an LLM. The reality is much deeper. There's an entire architecture working behind the scenes to make it all run.

The diagram below shows the full AI Agent Stack. At the core is the Agent Runtime that runs a ReAct loop, and three other layers feed into it.

AI Agent Runtime: The LLM thinks about what to do, picks a tool, observes the result, then reflects and decides the next step. This loop repeats until the goal is reached.

Model Layer (the brain): The underlying LLMs that power reasoning.

Tool Layer (the hands): How the agent interacts with the real world: search, APIs, code execution, data access.

Memory Layer (the notebook): Short-term working memory for the current task, long-term semantic memory for knowledge, and transactional memory for state.

Wrapping everything is the Observability & Safety Layer. This is what keeps agents debuggable, evaluable, cost-aware, and safe in production.

Over to you: Which layer of the stack do you think is the hardest to get right in production?

--
Subscribe to our weekly newsletter to get a Free System Design PDF (368 pages): https://go.bytebytego.com/nl-subscribe

#systemdesign #coding #interviewtips
.

Salesforce deployed 20,000 enterprise AI agents. The biggest lesson? The work is inverted! Traditional software → 90% of the effort comes before launch AI agents → 90% comes after We sat down with John Kucera, CPO of Agentforce, to learn what separates agents that deliver real value from those that stall after a good demo. Teams that treat launch as the finish line stay stuck in pilot mode. Teams that treat it as the starting line scale. The full playbook covers: - Why most enterprise agents f...

ByteByteGo — Wed, 10 Jun 2026 05:56:49 GMT

Salesforce deployed 20,000 enterprise AI agents. The biggest lesson? The work is inverted!

Traditional software → 90% of the effort comes before launch
AI agents → 90% comes after

We sat down with John Kucera, CPO of Agentforce, to learn what separates agents that deliver real value from those that stall after a good demo.

Teams that treat launch as the finish line stay stuck in pilot mode. Teams that treat it as the starting line scale.

The full playbook covers:
- Why most enterprise agents fail
- Pre-launch foundations (scope, KPIs, guardrails)
- The feedback loop that gates scaling
- 3 anti-patterns from 20,000 deployments
- Where agent architecture is heading next

Full breakdown: https://blog.bytebytego.com/p/what-sa...

#AI #AIEngineer #MachineLearning
.

We’re looking for multiple part-time instructors to teach AI and engineering cohort-based live courses. This is a great fit if you love teaching, enjoy sharing what you know, and want a meaningful side thing alongside your main work. The role has some upfront time investment to get familiar with the curriculum and prepare, but after that, it’s designed to be a limited commitment (2-5 hours bi-weekly). It offers stable income, good upside, and a chance to share your knowledge while working with...

ByteByteGo — Tue, 09 Jun 2026 05:56:49 GMT

We’re looking for multiple part-time instructors to teach AI and engineering cohort-based live courses.

This is a great fit if you love teaching, enjoy sharing what you know, and want a meaningful side thing alongside your main work.

The role has some upfront time investment to get familiar with the curriculum and prepare, but after that, it’s designed to be a limited commitment (2-5 hours bi-weekly). It offers stable income, good upside, and a chance to share your knowledge while working with ambitious learners.

We’re especially looking for instructors in:

- Building Production-Grade AI Systems
- System Design
- AI Security & LLM Red-Teaming
- AI Evals Intensive
- AI Cost Optimization
- Agentic AI Coding
- Build with Codex
- AI for Engineering Leaders
- AI Automation
- Others, please suggest

Ideal instructors are hands-on, clear communicators, and excited to teach.

If this sounds like you, email us at jobs@bytebytego.com with your background, the topics you’d be excited to teach, and any teaching, writing, or speaking samples.

#AI #AIEngineer #Systemdesign

How OpenAI Built Its Data Agent Most teams building data agents stack routers, fine-tunes, and complex retrieval pipelines on top of multiple LLMs. OpenAI didn't. Their data agent runs on a single model and only 13 tools, across 1.5 exabytes and 90,000 tables. It's "pretty vanilla" by design. We spoke with Emma Tang, Head of Data Platform Engineering at OpenAI, to better understand the architecture and the engineering decisions behind it. The article covers: - The architecture behind the data ag...

ByteByteGo — Tue, 09 Jun 2026 05:56:49 GMT

How OpenAI Built Its Data Agent

Most teams building data agents stack routers, fine-tunes, and complex retrieval pipelines on top of multiple LLMs. OpenAI didn't.

Their data agent runs on a single model and only 13 tools, across 1.5 exabytes and 90,000 tables. It's "pretty vanilla" by design.

We spoke with Emma Tang, Head of Data Platform Engineering at OpenAI, to better understand the architecture and the engineering decisions behind it.

The article covers:
- The architecture behind the data agent
- The six layers of context that make a single LLM reliable across 90,000 tables
- How OpenAI Uses Codex Internally: 3 Use Cases
- Five practical lessons for any team building a domain agent
- Where OpenAI's data platform is headed next

Read the full article here: https://blog.bytebytego.com/p/how-ope...

What is Google’s TPU? A TPU (Tensor Processing Unit) is Google’s custom AI chip, designed from scratch for the giant matrix multiplications that modern models live on. GPUs were built for graphics first. TPUs were built for deep learning from day one. At Cloud Next ’26, Google unveiled its 8th generation, and for the first time it ships in two flavors. TPU 8t is built for training, where raw throughput wins. TPU 8i is built for inference, where latency and chip-to-chip speed matter most. Both...

ByteByteGo — Tue, 02 Jun 2026 05:56:49 GMT

What is Google’s TPU?

A TPU (Tensor Processing Unit) is Google’s custom AI chip, designed from scratch for the giant matrix multiplications that modern models live on. GPUs were built for graphics first.

TPUs were built for deep learning from day one.

At Cloud Next ’26, Google unveiled its 8th generation, and for the first time it ships in two flavors. TPU 8t is built for training, where raw throughput wins. TPU 8i is built for inference, where latency and chip-to-chip speed matter most.

Both still share the same Axion CPUs, liquid cooling, and software stack, so code written for one runs on the other.

The diagram below is a quick study guide to what’s the same, what’s different, and why, based on our understanding of published Google articles.

--
Subscribe to our weekly newsletter to get a Free System Design PDF (368 pages): https://go.bytebytego.com/nl-subscribe

#systemdesign #coding #interviewtips
.

Latency vs Throughput vs Bandwidth Ever wondered why your app feels slow even when the bandwidth looks fine? Latency, throughput, and bandwidth often get used interchangeably, but each one tells a different story about performance. Latency is the delay. How long it takes for a single packet to travel from sender to receiver. If your ping shows 40 ms round-trip, that's latency. Throughput is the actual delivery rate. How much data is successfully transferred per second. If your download shows 62 ...

ByteByteGo — Tue, 02 Jun 2026 05:56:49 GMT

Latency vs Throughput vs Bandwidth

Ever wondered why your app feels slow even when the bandwidth looks fine? Latency, throughput, and bandwidth often get used interchangeably, but each one tells a different story about performance.

Latency is the delay. How long it takes for a single packet to travel from sender to receiver. If your ping shows 40 ms round-trip, that's latency.
Throughput is the actual delivery rate. How much data is successfully transferred per second. If your download shows 62 Mbps, that’s throughput.

Bandwidth is the maximum capacity of the link. For example, a 100 Mbps connection is the upper limit under ideal conditions.

Throughput is always less than bandwidth. Network congestion, packet loss, and protocol overhead all affect throughput, which is why you never actually hit the maximum bandwidth capacity in practice.

Similarly, low latency doesn't always mean high throughput. Small payloads, single connections, and tight window sizes can all keep throughput low, which is why fast responses don't guarantee you're sending a lot of data.

Another way to understand these three concepts: Bandwidth is the highway width. Throughput is the traffic flow. Latency is how long it takes a car to go from A to B.

All three matter, but they solve different problems.

Over to you: How do you measure these metrics in a way that actually predicts when things will break?

--
Subscribe to our weekly newsletter to get a Free System Design PDF (368 pages): https://go.bytebytego.com/nl-subscribe

#systemdesign #coding #interviewtips
.