New blog

app/blog/[slug]/page.tsx

@@ -10,7 +10,6 @@ import type { ComponentPropsWithoutRef, ReactNode } from 'react';
 import { TableOfContents } from '@/components/mdx/TableOfContents';
 import { SideNote } from '@/components/mdx/SideNote';
 import { Citation, Bibliography } from '@/components/mdx/Citation';
-import { MobileTableOfContents } from '@/components/mdx/MobileTableOfContents';
 import { formatPostDate } from '@/lib/format';
 
 const slugify = (text: string): string => {
@@ -261,8 +260,6 @@ export default async function BlogPost({ params }: Props) {
                         </p>
                     </header>
 
-                    <MobileTableOfContents headings={headings} />
-
                     <div className="essay-prose relative mt-8 max-w-none xl:max-w-[44rem]">
                         <MDXRemote
                             source={post.content}

app/blog/page.tsx

@@ -5,10 +5,10 @@ import { getAllPosts } from '@/lib/mdx';
 
 export const metadata: Metadata = {
     title: 'Blog',
-    description: 'Thoughts on software, deep learning, and research by Akshay Kolli.',
+    description: 'Essays and notes by Akshay Kolli on GPUs, ML hardware, deep learning, and research.',
     openGraph: {
         title: 'Blog',
-        description: 'Thoughts on software, deep learning, and research by Akshay Kolli.',
+        description: 'Essays and notes by Akshay Kolli on GPUs, ML hardware, deep learning, and research.',
     },
 };
 
@@ -22,10 +22,11 @@ export default function BlogIndex() {
                     <div className="space-y-4">
                         <p className="eyebrow">Writing</p>
                         <h1 className="max-w-[40rem] text-balance font-sans text-5xl font-medium leading-[0.96] text-ink sm:text-6xl lg:text-7xl">
-                            Notes on software, deep learning, and research.
+                            Notes on GPUs, models, and the details underneath.
                         </h1>
                         <p className="max-w-[34rem] text-[1rem] leading-8 text-muted">
-                            A small archive of ideas, experiments, and things worth slowing down enough to explain.
+                            Hardware quirks, deep learning systems, and research problems
+                            that needed more than the product page.
                         </p>
                     </div>
                 </header>
@@ -57,7 +58,7 @@ export default function BlogIndex() {
                                         </span>
                                     ))}
                                     <Link href={`/blog/${post.slug}`} className="text-ink transition-colors hover:text-accent">
-                                        Read essay
+                                        Read
                                     </Link>
                                 </div>
                             </div>
@@ -68,7 +69,7 @@ export default function BlogIndex() {
                 <section className="grid gap-4 border-t border-line pt-6 md:grid-cols-[8rem_minmax(0,1fr)]">
                     <p className="eyebrow md:pt-1">Archive</p>
                     <p className="max-w-[34rem] text-[0.96rem] leading-7 text-muted">
-                        {posts.length} published {posts.length === 1 ? 'essay' : 'essays'}. The archive stays selective.
+                        {posts.length} {posts.length === 1 ? 'essay' : 'essays'} published so far. I publish when I have a real question to chase down.
                     </p>
                 </section>
             </div>

app/layout.tsx

@@ -31,10 +31,10 @@ export const metadata: Metadata = {
     default: "Akshay Kolli",
     template: "%s | Akshay Kolli",
   },
-  description: "Personal website of Akshay Kolli — CS PhD Student at UMass Lowell researching World Models, Reinforcement Learning, and Multi-Agent Systems.",
+  description: "Akshay Kolli is a CS PhD student at UMass Lowell working on world models for RL, ML hardware, GPUs, and deep learning systems.",
   openGraph: {
     title: "Akshay Kolli",
-    description: "CS PhD Student at UMass Lowell researching World Models, Reinforcement Learning, and Multi-Agent Systems.",
+    description: "CS PhD student at UMass Lowell working on world models for RL, ML hardware, GPUs, and deep learning systems.",
     siteName: "Akshay Kolli",
     type: "website",
   },

app/page.tsx

@@ -12,16 +12,17 @@ export default function Home() {
     <div className="page-frame py-16 sm:py-20">
       <section className="mx-auto grid min-h-[calc(100vh-11rem)] max-w-[64rem] gap-6 lg:grid-cols-[minmax(0,39rem)_15rem] lg:items-center lg:gap-4">
         <div className="max-w-[39rem] space-y-6">
-          <p className="eyebrow">Akshay Kolli / Research + Writing</p>
+          <p className="eyebrow">Akshay Kolli / UMass Lowell</p>
 
           <div className="space-y-4">
             <h1 className="max-w-[34rem] text-balance font-sans text-5xl font-medium leading-[0.96] text-ink sm:text-6xl lg:text-7xl">
-              World models and reinforcement learning.
+              World models for RL.
             </h1>
 
             <div className="max-w-[32rem] space-y-3 text-[1.04rem] leading-8 text-ink-soft sm:text-[1.1rem]">
               <p>
-                I&apos;m a CS PhD student at UMass Lowell building world models for autonomous agents.
+                I&apos;m a CS PhD student working on world models for reinforcement learning,
+                ML hardware, GPUs, and deep learning systems.
               </p>
             </div>
           </div>

app/projects/graph-attention-topology/page.tsx

@@ -14,34 +14,34 @@ const paperLinks = [
 
 const highlights = [
   {
-    value: "No known graph",
-    label: "Learns topology without prior adjacency examples.",
+    value: "No graph labels",
+    label: "Learns from trajectories without seeing adjacency examples.",
   },
   {
     value: "Unknown dynamics",
-    label: "Uses observed states instead of hand-specified equations.",
+    label: "Works from observed state sequences, not hand-written dynamics.",
   },
   {
-    value: "Attention as edges",
-    label: "Interprets learned attention scores as the graph estimate.",
+    value: "Attention becomes edges",
+    label: "Uses attention scores as the topology estimate.",
   },
 ];
 
 const steps = [
-  "Embed every agent into a shared latent space.",
-  "Project embeddings into key/query vectors and compute pairwise attention.",
-  "Translate observed agent states into values.",
-  "Predict the next state and read the attention matrix as topology.",
+  "Put each agent in a shared latent space.",
+  "Build key/query pairs and compute attention between agents.",
+  "Use observed states as the values passed through attention.",
+  "Forecast the next state, then threshold attention to recover topology.",
 ];
 
 export const metadata: Metadata = {
   title: "Graph Attention Topology Inference",
   description:
-    "Project page for Graph Attention Inference of Network Topology in Multi-Agent Systems.",
+    "A project page for inferring hidden multi-agent network topology with graph attention.",
   openGraph: {
     title: "Graph Attention Inference of Network Topology in Multi-Agent Systems",
     description:
-      "A graph-attention approach for inferring hidden network topology from multi-agent trajectories.",
+      "Inferring hidden network topology from multi-agent trajectories with graph attention.",
     type: "article",
     publishedTime: "2024-10-26",
     images: [
@@ -117,7 +117,7 @@ export default function GraphAttentionProjectPage() {
                 Graph Attention Inference of Network Topology
               </h1>
               <p className="max-w-[43rem] text-[1.12rem] leading-8 text-graph-soft sm:text-[1.25rem]">
-                Inferring the hidden graph behind a multi-agent system by training attention to predict what each agent does next.
+                We use next-state prediction to expose the interaction graph behind a multi-agent system.
               </p>
             </div>
 
@@ -196,14 +196,14 @@ export default function GraphAttentionProjectPage() {
               <div className="space-y-5">
                 <p className="graph-kicker">Overview</p>
                 <h2 className="max-w-[42rem] text-balance text-4xl font-semibold leading-tight text-graph-ink sm:text-5xl">
-                  Predict the next state, then read the graph from what the model attended to.
+                  Train on motion. Read the graph from attention.
                 </h2>
                 <div className="space-y-5 text-[1.02rem] leading-8 text-graph-soft">
                   <p>
-                    The paper studies a practical problem in networked multi-agent systems: the agents move, synchronize, or converge, but the interaction graph behind that behavior is not given.
+                    In many multi-agent systems, you can watch agents move, synchronize, or settle, but you are not given who influences whom.
                   </p>
                   <p>
-                    The model is trained on state trajectories from consensus dynamics and Kuramoto oscillators. During prediction, attention scores between agent embeddings become an interpretable approximation of the adjacency matrix.
+                    This project tests whether a predictor trained on trajectories can recover that missing adjacency matrix. On consensus dynamics and Kuramoto oscillators, the learned attention scores become a usable proxy for edges.
                   </p>
                 </div>
               </div>
@@ -211,7 +211,7 @@ export default function GraphAttentionProjectPage() {
               <Figure
                 src={simulationImage}
                 alt="Consensus and Kuramoto simulation examples with adjacency matrices"
-                caption="Simulation examples: consensus dynamics and Kuramoto oscillators paired with their hidden adjacency matrices."
+                caption="Consensus and Kuramoto trajectories, each paired with the hidden adjacency matrix used for simulation."
                 className="rounded-md"
               />
             </section>
@@ -221,10 +221,10 @@ export default function GraphAttentionProjectPage() {
                 <div className="space-y-5">
                   <p className="graph-kicker">Method</p>
                   <h2 className="text-balance text-4xl font-semibold leading-tight text-graph-ink sm:text-5xl">
-                    A topology estimate falls out of the attention layer.
+                    The graph is never supervised directly.
                   </h2>
                   <p className="text-[1.02rem] leading-8 text-graph-soft">
-                    Instead of supervising the graph directly, the model learns to forecast the system. The attention matrix is then thresholded and compared with the true graph.
+                    The model learns the dynamics first. After training, we threshold the attention matrix and compare the inferred edges with the true graph.
                   </p>
                 </div>
 
@@ -243,7 +243,7 @@ export default function GraphAttentionProjectPage() {
               <Figure
                 src={architectureImage}
                 alt="Architecture diagram showing agent embeddings, key-query attention, values, predictions, and loss"
-                caption="Architecture: embeddings produce key/query vectors; observed states produce values; the attention matrix approximates adjacency."
+                caption="Model layout: embeddings create key/query vectors, states provide values, and attention is later read as adjacency."
                 className="rounded-md"
               />
             </section>
@@ -252,31 +252,31 @@ export default function GraphAttentionProjectPage() {
               <div className="max-w-[46rem] space-y-5">
                 <p className="graph-kicker">Results</p>
                 <h2 className="text-balance text-4xl font-semibold leading-tight text-graph-ink sm:text-5xl">
-                  Strongest graph recovery appears with smaller systems, and more simulations help larger systems.
+                  Smaller systems recover cleanly; larger ones improve with more simulations.
                 </h2>
                 <p className="text-[1.02rem] leading-8 text-graph-soft">
-                  F1 link-prediction scores are above a random baseline for both tested dynamics. Consensus dynamics are easier than Kuramoto oscillators, while additional simulation data improves larger-agent inference.
+                  F1 scores beat a random baseline for both dynamics. Consensus is the easier case; Kuramoto needs more data as the number of agents grows.
                 </p>
               </div>
 
               <Figure
                 src={resultsImage}
                 alt="F1 score results for consensus dynamics and Kuramoto oscillators"
-                caption="F1 results across system size and number of simulations for consensus dynamics and Kuramoto oscillators."
+                caption="F1 link-prediction results across system size and simulation count."
                 className="rounded-md"
               />
 
               <div className="grid gap-6 md:grid-cols-[minmax(0,0.78fr)_minmax(0,1fr)] md:items-start">
                 <div className="rounded-md border border-graph-line bg-graph-panel p-5">
-                  <p className="font-mono text-[0.72rem] uppercase text-graph-muted">training behavior</p>
+                  <p className="font-mono text-[0.72rem] uppercase text-graph-muted">training trace</p>
                   <p className="mt-3 text-[1.02rem] leading-8 text-graph-soft">
-                    Attention first learns the obvious self-dependencies, then gradually recovers inter-agent structure as training progresses.
+                    Early training mostly finds self-dependencies. Later epochs start assigning weight to the agent-to-agent links.
                   </p>
                 </div>
                 <Figure
                   src={attentionImage}
                   alt="Attention matrices over training epochs and predicted graphs"
-                  caption="Attention values through training stages, from true graph to predicted graph."
+                  caption="Attention during training, from the true graph reference to the final predicted graph."
                   className="rounded-md"
                 />
               </div>

app/projects/page.tsx

@@ -9,8 +9,8 @@ const projects = [
     title: "Graph Attention Inference of Network Topology",
     eyebrow: "MECC 2024 / Multi-Agent Systems",
     description:
-      "Inferring hidden interaction graphs from multi-agent trajectories by training attention to predict each agent's next state.",
-    status: "Paper project",
+      "A graph-attention model that recovers hidden interaction graphs from trajectory data while learning to predict the next state.",
+    status: "Published paper",
     year: "2024",
     image: graphAttentionImage,
     imageAlt: "Graph attention model architecture diagram",
@@ -20,10 +20,10 @@ const projects = [
 
 export const metadata: Metadata = {
   title: "Projects",
-  description: "Research projects and paper pages by Akshay Kolli.",
+  description: "Selected projects on world models, ML hardware, GPUs, deep learning, and the occasional multi-agent systems paper.",
   openGraph: {
     title: "Projects",
-    description: "Research projects and paper pages by Akshay Kolli.",
+    description: "Selected projects on world models, ML hardware, GPUs, deep learning, and the occasional multi-agent systems paper.",
   },
 };
 
@@ -35,10 +35,11 @@ export default function ProjectsPage() {
           <div className="space-y-4">
             <p className="eyebrow">Projects</p>
             <h1 className="max-w-[44rem] text-balance font-sans text-5xl font-medium leading-[0.96] text-ink sm:text-6xl lg:text-7xl">
-              Research pages and technical artifacts.
+              World models, GPUs, and deep learning systems.
             </h1>
             <p className="max-w-[36rem] text-[1rem] leading-8 text-muted">
-              A compact index of paper pages, experiments, and systems work.
+              Most of my current work sits around ML hardware, deep learning models,
+              and world models for RL. This topology paper is the multi-agent exception.
             </p>
           </div>
           <p className="font-mono text-[0.72rem] uppercase leading-6 text-muted-strong lg:text-right">
@@ -77,7 +78,7 @@ export default function ProjectsPage() {
                     href={project.href}
                     className="font-medium text-ink underline decoration-line-strong underline-offset-4 transition-colors hover:text-accent hover:decoration-accent"
                   >
-                    Open project
+                    Read project
                   </Link>
                   <div className="flex flex-wrap gap-3 font-mono text-[0.68rem] uppercase text-muted-strong">
                     {project.tags.map((tag) => (

app/resume/page.tsx

@@ -2,27 +2,27 @@ import type { Metadata } from 'next';
 
 export const metadata: Metadata = {
     title: 'Resume',
-    description: 'Experience, education, and technical skills of Akshay Kolli.',
+    description: "Akshay Kolli's resume: research, engineering, education, and technical skills.",
     openGraph: {
         title: 'Resume',
-        description: 'Experience, education, and technical skills of Akshay Kolli.',
+        description: "Akshay Kolli's resume: research, engineering, education, and technical skills.",
     },
 };
 
 const experience = [
     {
         title: 'PhD in Computer Science',
-        org: 'University of Massachusetts',
+        org: 'University of Massachusetts Lowell',
         period: 'Jan 2025 – Present',
         bullets: [],
     },
     {
         title: 'Research Assistant',
-        org: 'Exalabs, University of Massachusetts',
+        org: 'Exalabs, University of Massachusetts Lowell',
         period: 'Sep 2022 – Present',
         bullets: [
-            'Designing state-of-the-art ML pipelines for graph properties and trajectory forecasting.',
-            'Reduced multi-agent simulation time by 1000x.',
+            'Building ML pipelines for graph-property prediction and trajectory forecasting.',
+            'Cut multi-agent simulation time by 1000x.',
         ],
     },
     {
@@ -72,10 +72,10 @@ export default function ResumePage() {
                     <div className="space-y-4">
                         <p className="eyebrow">Resume</p>
                         <h1 className="max-w-[40rem] text-balance font-sans text-5xl font-medium leading-[0.96] text-ink sm:text-6xl lg:text-7xl">
-                            Experience, education, and technical depth.
+                            Research and engineering work.
                         </h1>
                         <p className="max-w-[34rem] text-[1rem] leading-8 text-muted">
-                            Research, engineering, and systems work across academia and industry.
+                            My background across ML research, simulation tooling, and production software.
                         </p>
                         <div className="flex flex-wrap gap-x-5 gap-y-2 text-[0.92rem] text-ink">
                             <a href="/files/Resume_latest.pdf" className="transition-colors hover:text-accent">