Just learnt something really cool - instruction level parallelism (ILP)
Modern CPUs commonly have hardware that detects independent instructions, and overlaps their execution
In this example, assuming the input values are already available, operations 1 and 2 are independent.
The CPUs hardware tracks these dependencies, checks for available resources, and can actually overlap these operations!
These operations might even execute out of order, but the CPU ensures to preserve the program's required behaviour.
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Learnt something cool about CPU caches and spatial locality
When a program reads an array element, the CPU can fetch a whole cache line containing nearby elements too
So when the program reads the next element, it might already be in cache
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Elite tweet
So - one of my close friends has had the most heartbreaking experience of his lifetime with someone who he thought was his girlfriend.
Man got played like a PS4 in Covid y'all, he was talking to this girl for about 8 months, and he really thought that he's got her undivided attention, and secretly, he fell in love with her. He really thought that he has all of her heart, soul and spirit, but then - doomsday arrived for him.
It turns out, this girl had him in genjutsu of the HIGHEST order. She was talking to not one, not two, but THREE lads at the same damn time. And all those thought that they were the only ones who had her - JUST like multiple processes believe they have some control and access over the entire memory space, while in reality, it's all just virtual memory space that is mapped to physical memory that's managed by something called to MEMORY MANAGEMENT UNIT
Now that you are here, let's see how this plays out in the first place -
Whenever you execute a program, the OS creates a process with its own virtual address space. Instead of directly accessing physical RAM, the process operates using virtual addresses.
So, Process A can access virtual address 0x1000, and Process B can access that exact same address, yet both can refer to completely different physical locations.
How?
The MMU translates virtual addresses into physical addresses using information maintained in page tables. Memory is divided into fixed-size blocks called pages (virtual memory) and frames (physical memory).
For example, with 4 KiB pages:
Virtual address: 0x1234
Virtual page: 0x1
Page offset: 0x234
If virtual page 0x1 maps to physical frame 0x8, the resulting physical address is:
0x8000 + 0x234 = 0x8234
Simple enough, right?
But looking up page tables for every memory access would be expensive. Here, we et help from something called as the Translation Lookaside Buffer (TLB) - a small, fast hardware cache that stores recently used address translations.
A TLB hit means the translation is readily available. A TLB miss means the system needs to retrieve it through the applicable page-table-walking mechanism. And no, a TLB miss is NOT the same as a page fault.
A page fault occurs when a memory access requires OS intervention - perhaps to allocate a page, retrieve it from storage, establish a mapping, or reject an invalid access. But virtual memory does more than translate addresses.
It provides process isolation and memory protection. Page-table permissions control whether a process can read, write, or execute a particular memory region. This prevents one process from casually accessing or corrupting another process's private memory.
And here's another interesting bit: virtual memory doesn't mean every process gets its own dedicated chunk of physical RAM.
Processes can share physical pages containing read-only executable instructions, use memory-mapped files, or employ mechanisms such as copy-on-write to avoid unnecessary duplication.
So, bringing it all together:
->Virtual address spaces give each process its own view of memory.
->Page tables define how virtual pages map to physical frames.
->The MMU performs address translation and enforces access permissions.
->The TLB caches translations to speed things up.
Page faults allow the OS to handle memory accesses that need additional intervention. The beauty of virtual memory is that a process doesn't need to know where its data actually resides in physical RAM.
It simply works with its own virtual address space.
And that's abstraction in computer architecture for you. A relatively simple interface hiding an extraordinary amount of complexity underneath.
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Had a discussion around memory staleness recently, and it's a notoriously hard problem to "solve"
- What does a "stale" memory even mean?
- Do you outright delete old memories? Assign lower "weights" to them?
- How do you even decide if a memory is stale in the first place?
- What do you do with a memory that keeps getting retrieved, but never gets used in LLM responses?
- Do LLMs need to know how old/how "stale" a memory is?
- How do you determine the "importance" of a memory?
- Are there multiple "stages" to staleness in memory? Or is this binary?
- Can you "un-stale" an older/lesser used memory or make it "fresh"?
Just way too many questions left unanswered.
I guess the real answer could be just that it depends on what you're building, but even then it's a hard problem to truly solve.
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Great use case of Jev 👏
Introducing Jev @typesafeai powered social-api
Try it at treg.to/social
- One API to access any social data
- Track every mention of you & your competitors
- Find trending posts in your niche, filtered by Jev
- Under $0.05 to pull 1,000 competitor ads
Open source, 0% markup
👇 Repo link below
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Not bad, considering I lost 1-2k commits in aug-sep after leaving an org
Can I get to 10k tho 👀
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Getting interviews used to be purely a volume game, because we didn't have time for custom resumes and cover letters for each company.
But now it's turned into a volume AND quality game. Volume remains important, but with AI, you can genuinely customize each application.
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Would love to have some conversations with folks working at the edge of voice AI
Ideally both of us learn something from the call
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Prasad Sankar retweeted
Introducing Libretto - voice workflows written in YAML
Define the stages, control the transitions, and require confirmation before actions. Inspect the flow visually, then test it over voice or text.
Open source. Try it now: libretto.prasadjs.me
Been iterating on the design of libretto.prasadjs.me on @paper and it's been an absolute life-changer. Makes things so much easier when I can review design first and then implement it
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How I made voice agent more predictable:
prasadjs.me/blog/building-li…
Significantly better workflow visualization, now on libretto.prasadjs.me
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Soniox just does not stop winning man
Introducing Soniox STT v6, the world’s first speech recognition model that knows what your voice agent is saying and automatically filters out the agent’s own voice.
Two connected innovations make this possible:
→ Agent text input gives recognition context from both sides of the conversation, improving its understanding of user responses.
→ Agent voice echo cancellation filters out the agent’s playback while continuing to listen to the user, even when they interrupt.
The result: fewer false interruptions, fewer clarification loops, and more reliable conversations.
V6 also delivers significantly higher accuracy across 60+ languages for voice typing, meeting transcription, multilingual recognition, and real-time translation, with stronger speaker diarization, language identification, endpoint detection, and context customization.
One unified speech model. All 60+ languages. The full feature set, with languages freely mixed.
Available now for real-time streaming and asynchronous transcription.
Explore what’s new in Soniox STT v6:
soniox.com/blog/soniox-stt-v…
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Had an interview today and got rejected.
But had a realization - decision making is one of the most important traits you can have as an engineer. You can't always make a subpar decision and expect to be able to correct it later when something goes wrong
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Been using T3 recently, one simple but very useful UX feature I noticed in @t3dotcodes
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Prasad Sankar retweeted
I agree! As someone in healthcare, this is the biggest barrier to AI adoption in healthcare. AI can give a diagnosis, but we would need to know why, otherwise that’s super scary for everyone involved (the patient, the healthcare professional using the AI tool, etc).
Updated home page to explain what the product is all about: libretto.prasadjs.me
Working on updating the studio page itself, to make it so much easier to make deterministic voice agents
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