Last week I wrote about how Vine, Twitter’s new “Instagram of video”, offers an impeccably designed user experience backed up by engineering that delivers the only thing that matters in mobile: speed.
I asked Twitter to comment on the technology solutions that let Vine’s 6-second videos load as fast as photos on a smartphone–or close enough. Here’s what Colin Kroll, leader of Vine’s engineering team, said:
- Compress early, compress often. “We begin the encoding process in Vine as soon as you start recording [a clip]. Since we’re encoding to h.264, we also benefit from hardware acceleration in the iPhone and iPod Touch chipsets. Videos that are uploaded to Vine are transcoded to different bit rates on our servers for different connectivity scenarios.
- Forget HD. “Videos on Vine are recorded at 480x480 using the H264 codec. The frame rate is variable, depending on your device and lighting conditions, but generally clocks in around 22-30FPS.”
- Stay two steps ahead of the user. “Vine keeps a running estimate of a device’s connectivity, and loads videos with the appropriate bit rate. We do have an algorithm in place that will preload up to two subsequent posts [in the app], based on the activity of the person using it.”
None of this sounds like rocket science, but solving the “Instagram of video” problem was never about a achieving a technological moon shot. It was about identifying which part of the problem actually mattered – what is the primary experience that people want out of social video on their phones? – and aiming Twitter’s highly-scaled technology squarely at that target. This synergy of design strategy and engineering tactics is what creates successful technology experiences that seem “obvious” in hindsight.
The dark secret behind those cute AI-generated animal images
Google Brain has revealed its own image-making AI, called Imagen. But don't expect to see anything that isn't wholesome.
Inside Charm Industrial’s big bet on corn stalks for carbon removal
The startup used plant matter and bio-oil to sequester thousands of tons of carbon. The question now is how reliable, scalable, and economical this approach will prove.
The hype around DeepMind’s new AI model misses what’s actually cool about it
Some worry that the chatter about these tools is doing the whole field a disservice.
How Charm Industrial hopes to use crops to cut steel emissions
The startup believes its bio-oil, once converted into syngas, could help clean up the dirtiest industrial sector.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.