DEV Community: Jacob Zhang

Your ranking model can be perfect offline and still fail the interview

Jacob Zhang — Sun, 25 Oct 2026 00:03:19 +0000

Your ranking model can be perfect offline and still fail the interview. The interview isn't testing your model. It's testing your judgment about it.

Design an ads ranking system, and the structure interviewers want to hear is boring on purpose — four stages, no magic:

Candidate generation first, ranking second. You can't score a billion ads. Retrieval (two-tower embeddings, ANN index) cuts billions to hundreds. If you start with the ranker, you've already failed the scale question.
Ranking in two passes. A light model scores hundreds, a heavy model re-ranks tens. Say why: latency budget. A 100ms p99 budget decides your model size more than accuracy does.
Features in three buckets. User, item, context — and one cross feature that actually matters (user-item interaction history). Listing 30 features is worse than defending 5.
Metrics that disagree. Offline AUC/NDCG up, online revenue flat — that happens, and the interviewer wants your diagnosis: selection bias in training data, position bias in clicks, or a delayed feedback loop. Pick one, explain the fix, don't just say "run an A/B test."

The skeleton never changes: retrieval → light rank → heavy rank → calibration. What changes is which trade-off you defend.

The full walkthrough of this question and hundreds more like it are in the DeepOffer question bank.

Muse invite code DYGM1I: 7 questions every new user asks, answered

Jacob Zhang — Fri, 23 Oct 2026 18:54:57 +0000

1 billion free AI tokens. No credit card. No catch. Two minutes to claim.

Muse is the #1 Productivity app on the US App Store — 4.9 stars from 300,000+ ratings.

I have been answering the same questions about the Muse invite program all week, so here are the seven I hear most:

1. What is Muse?
Meta's personal AI agent — chat, voice, and task help in one app.

2. What does the invite code do?
New users who enter a code within 48 hours of signing up get 1 billion Muse tokens, and the inviter gets the same. The tokens never expire.

3. Do I need a credit card?
No. During age verification you get three options — linking Facebook is the fastest and skips the $1 card pre-authorization entirely.

4. Why does signup say my region is unavailable?
Muse registration currently only accepts US and Canada IPs, and it is picky: datacenter IPs get rejected. Use a US residential IP and it goes through. Sign up at muse.ai/join.

5. Where exactly do I enter the code?
Settings → Redeem invite code. Type DYGM1I. It must be within 48 hours of creating your account — after that the field is gone.

6. What is your code?
DYGM1I. Use it if you need one.

7. What happens after I redeem?
Your own invite code appears under the Invite button, and you can start sharing it.

Meta's Muse AI gives new users 1 billion free tokens — the 2-minute setup

Jacob Zhang — Fri, 23 Oct 2026 06:48:35 +0000

Almost nobody knows this yet: a new AI app is handing out 1 billion free tokens to new users.

Muse is the #1 Productivity app on the US App Store — 4.9 stars from 300,000+ ratings.

Here's the exact 2-minute setup, including the two places people get stuck:

Step 1 — Sign up at muse.ai/join with a US residential IP.
The region check only looks at your exit IP. Datacenter IPs get "region unavailable"; a residential connection works. Signup is currently open to US/Canada IPs only.

Step 2 — Age verification: link Facebook.
You get three options. Linking Facebook is the fastest and skips the $1 card pre-authorization.

Step 3 — Redeem within 48 hours (the step everyone forgets).
Go to Settings → Redeem invite code and enter DYGM1I. Both of us get 1 billion Muse tokens that never expire. After 48 hours the window closes, so do it right away.

Once redeemed, your own invite code shows up under the Invite button — share it and keep the chain going.

Don't throw away 1 billion free Muse tokens: 5 sign-up mistakes to avoid

Jacob Zhang — Thu, 22 Oct 2026 19:23:13 +0000

Muse is the #1 Productivity app on the US App Store — 4.9 stars from 300,000+ ratings.

Muse (Meta's personal AI agent) hands out 1 billion free tokens to both sides of an invite — but only if you do everything right in the first 48 hours. Here are the five mistakes that cost people their tokens:

Signing up on a datacenter IP. Muse only accepts US/Canada registrations and it checks your exit IP. Most VPNs use datacenter ranges and you'll see "region unavailable". Use a US residential IP.
Skipping past the code. After registering, you have 48 hours to go to Settings → General → Redeem invite code and enter a code (mine is DYGM1I). Miss the window and the code can't be applied retroactively. Do it immediately.
Picking the wrong age-verification option. The flow asks for age verification; linking Facebook is the quickest way through and skips the $1 credit-card pre-authorization.
Typing the code wrong. Codes are case-sensitive — it's DYGM1I (that's D-Y-G-M-1-I).
Waiting to "try it first". The 48-hour clock starts at sign-up, not at first use. Register, verify, redeem — then explore.

Steps: muse.ai/join (US residential IP) → age verification via Facebook → within 48h: Settings → Redeem invite code → DYGM1I → 1 billion tokens for you and the inviter, never expire.

How to Register for Muse AI and Claim 1 Billion Free Tokens

Jacob Zhang — Thu, 22 Oct 2026 05:53:01 +0000

Meta's personal AI agent Muse is only open to US/Canada IPs right now, and registration trips a lot of people up. Two things that matter:

Muse is the #1 Productivity app on the US App Store — 4.9 stars from 300,000+ ratings.

Use a residential US IP. Datacenter IPs get flagged as "region unavailable". A regular VPN often fails; a residential connection works.
Age verification: you get three options. Linking Facebook is the fastest way to skip the $1 card pre-authorization.

After you sign up at muse.ai/join, don't forget the time-sensitive step: within 48 hours, go to Settings → General → Redeem invite code and enter an invite code — both you and the inviter get 1 billion Muse tokens that never expire.

My code is DYGM1I if you need one. After redeeming, your own invite code appears under the Invite button — share it and keep the chain going.

3 Hard Lessons from Building a Voice-Based AI Mock Interviewer

Jacob Zhang — Thu, 08 Oct 2026 04:38:30 +0000

I spent the last few months building a voice-based mock interview tool for ML engineers. The idea sounded simple: an AI interviewer that talks to you, asks follow-ups, and scores your answers.

The reality was harder than expected. Here are the three lessons that cost me the most time.

1. Latency is the whole product

In text chat, users forgive a 3-second reply. In voice, a 2-second pause kills the conversation. The interview feels broken.

I used the OpenAI Realtime API, which streams audio with low latency. But the API alone was not enough. I had to:

Start generating the reply while the user is still finishing their sentence (careful turn detection).
Keep the spoken replies short. Long answers sound robotic and make the user wait.
Handle interruptions. Real interviewers get interrupted. The system needs to stop talking and listen.

If you are building anything voice-first, budget half your engineering time for latency. It is not a detail. It is the product.

2. Scoring AI answers needs guardrails, or it lies to you

My first scoring version gave a 7/10 to a 5-second "I don't know." That is worse than no score at all — it teaches the user nothing and destroys trust.

What worked:

Hard gate: no score unless the user spoke substantively for at least 60 seconds.
A strict rubric: 5/10 is a solid average, 8+ is genuinely excellent. The prompt explicitly forbids inflating scores for vague answers.
Five separate dimensions (correctness, depth, clarity, structure, communication) instead of one number. A single score hides everything.

Honest scoring is a feature. Users can tell when they are being flattered.

3. Follow-up questions must come from the conversation, not a script

A fixed list of follow-ups feels like a quiz. A real interviewer listens and probes deeper into what you just said.

I generate follow-ups from the full conversation history. The interviewer tracks the candidate's reasoning and asks about the weakest point. This was the hardest part to get right — it needs enough context to be smart, but not so much that it rambles.

The test I use: if you removed the AI label, would this feel like a human interviewer? If yes, ship it.

These lessons come from building DeepOffer (https://getdeepoffer.com), a voice mock interview platform for MLE interviews. If you are preparing for interviews, the free question bank is a good place to practice. And if you are building voice AI yourself, I would love to compare notes in the comments.

I Collected 2026's Google Interview Reports. Here's What's Actually Getting Asked.

Jacob Zhang — Wed, 30 Sep 2026 06:11:23 +0000

Google's coding interviews have a reputation for being unpredictable. After going through publicly shared 2026 interview reports (Glassdoor, Blind, LeetCode Discuss, July–September), I'd say they're not random at all — the structure is actually pretty predictable. Here's what keeps showing up.

A quick note on honesty: dated first-hand reports from the last few months are scarce. Most of what's public is pattern-level, not exact questions. Below I separate confirmed sightings (a report explicitly named or clearly described the exact problem) from high-frequency patterns (the report described the shape; these are Google's classic favorites under that shape). Don't trust anyone selling you "the exact 2026 question list" — including me.

1. Graphs are the #1 topic this year

Island-counting problems (the LeetCode 200 family) keep appearing as round openers — but they're just the warm-up. The real test is the follow-up, where the interviewer adds dynamic constraints mid-round ("now land appears over time — track the island count as it changes").

Also showing up: grid traversal where your movement depends on where you came from (state is more than just coordinates), and ranking-from-pairwise-results problems, which are topological sort in disguise.

If you grind one chapter, make it graphs: 200, 695, 994, 417, 329, 207, 210, 269.

2. The DP → binary search arc

The most 2026 thing I saw: you start with a DP formulation, and the interviewer keeps pushing — find the bottleneck, optimize — until you land on a binary search solution. The textbook example is the "painter's partition" problem (LC 410): the DP is O(n²k), the expected answer is binary-search-the-answer plus a greedy check.

Train this as a template, not a trick: 410, 1011, 1482 are the same muscle. If your DP solution passes without a follow-up asking "can you do better," something went wrong.

3. Trie + HashMap combos

One August report described a medium-hard Trie problem combined with hash maps — and it earned a Strong Hire. The lesson: practice the two together, not in isolation. 208 (implement it cold), 212, 421.

4. Union-Find gets grilled on correctness

Interviewers aren't just checking that you use DSU — they're digging into path compression, union-by-rank, and whether you can argue correctness or offer alternative traversals. 684 until it's muscle memory, then 990, 128.

5. Streams and heaps

Moving averages and top-K over data streams keep appearing in both SWE and MLE rounds. 346 was a confirmed sighting; the two-heap median template (295) and Kth-largest (703, 215) round it out.

The confirmed-sighting shortlist

These eight were explicitly named or clearly described in 2026 reports, so they're the safest bets if you're short on time: 200, 416, 174, 410, 2188, 128, 346, 315.

I turned the full research into an 81-problem checklist, ranked by 2026 frequency — every problem labeled as confirmed vs. pattern-matched, each with its source and a prep tip. It's $19 on Gumroad if you want the whole thing: https://mleplaybook.gumroad.com/l/google-leetcode-interview-checklist-2026

Good luck out there. And if you've interviewed at Google recently, drop what you saw in the comments — I'll fold it into the next update.