amul.exe ✓
@amuldotexe · heap · joined 25 May 2019
Analytics → Product Manager → Product Engineer | x: @flipkart, @EA, @gojektech, @Target | notes to self | take everyone lightly especially yourself
39 574Followers
1 060Following
73 171Posts total
28.8KViews on collected posts
Postingan terbaru
Mencari postingan baru…
first job in 2010 paid Rs 18k per month as junior lecturer mathematics @aakash institute
584 views · 5 likes
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Mera kuch samaan, tumhare paas pada hai; saavan ke kuch bheege bheege din ralhe hain; aur mere ek khat mein lipti raat padi hai ; vo raat bujha do, mera woh samaan lauta do;
612 views · 6 likes
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14/n
Can we make graph compute deterministic?
with knight bus - the bet is to turn estimation into an enforceable workload contract
I can think of an idea where, if we think that the RAM is being exceeded, then we limit the RAM and change the latency. It's okay to have no
132 views · 0 likes
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13/n
Path ahead:
This work needs the context of
A. deep problem solver on tech side +
B. deep empathy for adoption with enterprise
to be in the same context window of a single human
when we get enough adoption that this can be separated out, we will think of more humans who
148 views · 0 likes
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12/n
Also why me - why am I treating this as a blue ocean
"Many deep infra people optimize the engine. My wedge is to optimize the adoption loop: quote, run, receipt, trust."
A lot of HQ engineers great at optimization cannot empathize with what the customers really want -
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11/n
First Customer: Do not start where the TAM is largest. Start where the learning loop is shortest and the customer can hand you a graph tomorrow.
JTBD: When I have a large graph artifact and a high-stakes graph question, help me know whether I can run the algorithm safely h
85 views · 0 likes
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10/n
The demand for graphs will increase because agents will wield them - and absorb the complexity of context that humans previously needed to use them https://t.co/762s1tPERx
89 views · 0 likes
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9/n
Trying to sharpen my GTM pitch which is the most important piece of the puzzle https://t.co/jGCnFzhoZk
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8/n
The idea was not just analytical queries - it could be a message queue, a backend - could be anything that needed deterministic compute be it in terms of time or resources needed https://t.co/6OmGsJ9Ahm
132 views · 0 likes
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7/n
This idea of trying to create deterministic computation systems is not new to me
I previously ideated on RustHallows - a customized OS for heavy workload applications https://t.co/m9BUU6KAGT
124 views · 0 likes
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5/n
GTM Pitch iterated:
Knight Walker is memory-honest graph compute. It helps teams run graph algorithms without memory roulette: quote the job before execution, run it in a bounded way, and produce a receipt after. The first wedge is not replacing Neo4j; it is making graph
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4/n
I am not trying to restart the graph database hype cycle.
The GTM wedge is much narrower: memory-honest graph compute for teams whose work is already graph-shaped, but whose adoption is blocked by late failures, RAM provisioning, and uncertain cloud cost.
The first product
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2/n
why we need this https://t.co/gtH2X0kQRz
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@amuldotexe Check out https://t.co/ML5uNP4a6s
We don’t need Neo4j at all, like, ever.
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Why I am rewriting Neo4j in Rust
as per my research all OLAP graph runtimes just load everything in RAM and run it, so the cost of cloud is actually prohibitive, maybe thats why despite their utility graph algorithms did not really fly https://t.co/JJ5K0Pt0JN
21.1K views · 88 likes
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Dibandingkan akun berukuran sama
21 postingan dari 90 hari terakhir, dibandingkan dengan rentang 10K–100K pengikut. di bawah akun sekelasnya baik pada jangkauan maupun interaksi.
Median tayangan148akun ini1 033median untuk 10K–100K
Jangkauan, %0.37%akun ini3.60%median untuk 10K–100K
Interaksi, %0.94%akun ini1.69%median untuk 10K–100K
| Metrik | Akun ini | Median untuk 10K–100K | Rasio |
|---|---|---|---|
| Median tayangan per postingan | 148 | 1 033 | 0.14× |
| Jangkauan (tayangan ÷ pengikut) | 0.37% | 3.60% | 0.10× |
| Tingkat interaksi | 0.94% | 1.69% | 0.55× |
Akun lain pada rentang ini → Bandingkan dengan akun lain → Bagaimana tolok ukur ini disusun →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
1245 Aug
132
95
89
85
90
148
132
1.7K4 Sep
9005 Sep
958
755
612
584
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.81%5 Aug
0.76%
1.05%
1.12%
1.18%
1.11%
0.68%
0.00%
0.93%4 Sep
0.44%5 Sep
0.94%
0.66%
1.14%
1.03%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.69%.
What the audience does
Likes59.9%130 in total
Reposts1.4%3 in total
Replies15.7%34 in total
Quotes2.3%5 in total
Bookmarks20.7%45 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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